Patient Trendfollower (7)(alpha)Patient Trendfollower consists of 21 and 55 EMA, Commodity Channel Index and Supertrend indicator. It confirms a trend and gives you a signal on a pullback. Original creation worked on 1h EURUSD chart.
►Long setup:
• 21 EMA is above 55 EMA, which is above the Supertrend indicator.
• Commodity Channel Index is an oscillator, which prints into the chart if extreme levels are reached. Green is for a level above 100 or below -100, red is above 140 or below -140 and black is above 180 or below -180.
• If 21 EMA > 55EMA > Supertrend and an oversold signal appear, you can buy into the trend.
• When backtesting on 1h EURUSD, profit target 400 pips worked best with a stop-loss below Supertrend's bottom and the size of your spread.
• A picture shows two valid entries.
: This part still malfunctions and shows red dots over some green ones. It is important to disable red ones in the settings to see green ones.
Some more long signals:
Some short signals:
►Backtesting data with default settings and trading only green CCI signals with mentioned risk management strategy:
• 212 closed trades
• 58.96% profitable with average win trade 348 USD and average loss trade 263 USD when only green signals are followed.
• Profit factor 1.903, Sharpee 0.792
• 20 bars is average for all trades, short trades were 18 bars long on average.
With given data, you can see the strategy is profitable by itself. However, original risk management settings do work only on 1h charts of EURUSD and would need to be adjusted for other instruments based on average volatility.
Even though the profitability is low, you can increase your odds by a great margin, if you properly use price action (impulsive and corrective moves, patterns, bar analysis), if you trade when major exchanges are open, you may also use wave analysis such as Elliot Waves or Market Profiles to predict whether the next day might be a trending day. My backtesting program didn't consider these ideas.
Unfortunately, I won't be making backtesting strategy public with it anytime soon, because it still has some parts that do not work. I am ok with that since I understand the code and know what does malfunction and how. Then, there are parts which I am not sure how to fix yet. This is why the indicator is still considered alpha.
In the future when a strategy is published, you will also be able to set your own overbought/oversold values without entering the code itself and probably some other features. But I am not in a hurry for that. You can give me feedback on UX and try to figure out the best setups for other symbols, it might help to improve the automatic testing script when I know what I should achieve. My main point is to make this public for friends who can already be using it on EURUSD at least.
Close doesn't always have to be 400 pips, you might want to close on a logical level such as strong resistance or a trendline too.
Thanks to:
• @everget for providing Supertrend solution.
• Satik FX who hand-tested the system by hand and reported results in this article . He is my main inspiration for creating the complete indicator as one because I want to be able to show and hide it with a single click. My future scripts will also work as a whole strategy each by itself.
• The number in the script's name comes from Satik's numbering. A mentioned article was his seventh shared strategy.
스크립트에서 "backtesting"에 대해 찾기
HatiKO EnvelopesPublished source code is subject to the terms of the GNU Affero General Public License v3.0
This script describes and provides backtesting functionality to internal strategy of algorithmic crypto trading software "HatiKO bot".
Suitable for backtesting any Cryptocurrency Pair on any Exchange/Platform, any Timeframe.
Core Mechanics of this strategy are based on theory of price always returning to Moving Average + Envelopes indicator (Moving_average_envelope from Wiki)
Developement of this script and trading software is inspired by:
"Essential Technical Analysis: Tools and Techniques to Spot Market Trends" by Leigh Stevens (published on 12th of April 2002)
"Moving Average Envelopes" by ChartSchool, StockCharts platform (published on 13th of April 2015 or earlier)
"Коля Колеснік" from Crypto Times channel ("Метод сетка", published on 19th of August 2018)
"3 ways to use Moving Average Envelopes" by Rich Fitton, published on Trader's Nest (published on 28st of November 2018 or earlier)
noro's "Robot WhiteBox ShiftMA" strategy v1 script, published on TradingView platform (published on 29th of August 2018)
"Moving Average Envelopes: A Popular Trading Tool" Investopedia article (published 25th of June 2019)
and KROOL1980's blogpost on Argolabs ("Гридерство или Сетка как источник прибыли на форекс", published on 27th of February 2015)
Core Features:
1) Up to 4 Envelopes in each direction (Long/Short)
2) Use any of 6 different basis MAs, optionally use different MAs for Opening and Closure
3) Use different Timeframes for MA calculation, without any repainting and lookahead bias.
4) Fixed order size, not Martingale strategy
5) Close open position earlier by using Deviation parameter
6) PineScript v4 code
Options description:
Lot - % from your initial balance to use for order size calculation
Timeframe Short - Timeframe to use for Short Opening MA calculation, can be chosen from dropdown list, default is Current Graph Timeframe
MA Type Short - Type of MA to use for Short Opening MA calculation, can be chosen from dropdown list, default is SMA
Data Short - Source of Price for Short Opening MA calculation, can be chosen from dropdown list, default is OHLC4
MA Length Short - Period used for Short Opening MA calculation, should be >=1, default is 3
MA offset Short - Offset for MA value used for Short Envelopes calculation, should be >= 0, default is 0
Timeframe Long - Timeframe to use for Long Opening MA calculation, can be chosen from dropdown list, default is Current Graph Timeframe
MA Type Long - Type of MA to use for Long Opening MA calculation, can be chosen from dropdown list, default is SMA
Data Long - Source of Price for Long Opening MA calculation, can be chosen from dropdown list, default is OHLC4
MA Length Long - Period used for Long Opening MA calculation, should be >=1, default is 3
MA offset Long - Offset for MA value used for Long Envelopes calculation, should be >= 0, default is 0
Mode close MA Short - Enable different MA for Short position Closure, default is "false". If false, Closure MA = Opening MA
Timeframe Short Close - Timeframe to use for Short Position Closure MA calculation, can be chosen from dropdown list, default is Current Graph Timeframe
MA Type Close Short - Type of MA to use for Short Position Closure MA calculation, can be chosen from dropdown list, default is SMA
Data Short Close - Source of Price for Short Closure MA calculation, can be chosen from dropdown list, default is OHLC4
MA Length Short Close - Period used for Short Opening MA calculation, should be >=1, default is 3
Short Deviation - % to move from MA value, used to close position above or beyond MA, can be negative, default is 0
MA offset Short Close - Offset for MA value used for Short Position Closure calculation, should be >= 0, default is 0
Mode close MA Long - Enable different MA for Long position Closure, default is "false". If false, Closure MA = Opening MA
Timeframe Long Close - Timeframe to use for Long Position Closure MA calculation, can be chosen from dropdown list, default is Current Graph Timeframe
MA Type Close Long - Type of MA to use for Long Position Closure MA calculation, can be chosen from dropdown list, default is SMA
Data Long Close - Source of Price for Long Closure MA calculation, can be chosen from dropdown list, default is OHLC4
MA Length Long Close - Period used for Long Opening MA calculation, should be >=1, default is 3
Long Deviation - % to move from MA value, used to close position above or beyond MA, can be negative, default is 0
MA offset Long Close - Offset for MA value used for Long Position Closure calculation, should be >= 0, default is 0
Short Shift 1..4 - % from MA value to put Envelopes at, for Shorts numbers should be positive, the higher is number, the higher should be Shift position, example: "Shift 1 = 1, shift 2 = 2, etc."
Long Shift 1..4 - % from MA value to put Envelopes at, for Longs numbers should be negative, the lower is number, the lower should be Shift position, example: "Shift 1 = -1, shift 2 = -2, etc."
From Year 20XX - Backtesting Starting Year number, only 20xx supported as script is cryptocurrency-oriented.
To Year 20XX - Backtesting Final Year number, only 20xx supported as script is cryptocurrency-oriented.
From Month - Years starting Month, optional tweaking, changing not recommended
To Month - Years ending Month, optional tweaking, changing not recommended
From day - Months starting day, optional tweaking, changing not recommended
To day - Months ending day, optional tweaking, changing not recommended
Graph notes:
Green lines - Long Envelopes.
Red lines - Short Envelopes.
Orange line - MA for closing of Short positions.
Lime line - MA for closing of Long positions.
**************************************************************************************************************************************************************************************************************
Опубликованный исходный код регулируется Условиями Стандартной Общественной Лицензии GNU Affero v3.0
Этот скрипт описывает и предоставляет функции бектеста для внутренней стратегии алгоритмического программного обеспечения "HatiKO bot".
Подходит для тестирования любой криптовалютной пары на любой бирже/платформе, на любом таймфрейме.
Кор-механика этой стратегии основана на теории всегда возвращающейся к значению МА цены с использованием индикатора Envelopes (Moving_average_envelope from Wiki)
Разработка этого скрипта и программного обеспечения для торговли вдохновлена следующими источниками:
Книга "Essential Technical Analysis: Tools and Techniques to Spot Market Trends" Ли Стивенса (опубликовано 12 апреля 2002 года)
«Moving Average Envelopes» от ChartSchool, платформа StockCharts (опубликовано 13 апреля 2015 года или раньше)
«Коля Колеснік» с канала Crypto Times («Метод сетка», опубликовано 19 августа 2018 года)
«3 ways to use Moving Average Envelopes» Рича Фиттона, опубликованные в «Trader's Nest» (опубликовано 28 ноября 2018 года или раньше)
Скрипт стратегии noro "Robot WhiteBox ShiftMA" v1, опубликованный на платформе TradingView(опубликовано 29 августа 2018 года)
«Moving Average Envelopes: A Popular Trading Tool», статья Investopedia (опубликовано 25 июня 2019 года)
Блог KROOL1980 из Argolabs («Гридерство или Сетка как источник прибыли на форекс», опубликовано 27 февраля 2015 года)
Основные особенности:
1) До 4-х Ордеров в каждом из направлении (Лонг / Шорт)
2) Выбор из 6-ти разных базовых МА, опционально используйте разные МА для открытия и закрытия.
3) Используйте разные таймфреймы для расчета MA, без перерисовки и "эффекта стеклянного шара".
4) Фиксированный размер ордера, а не стратегия Мартингейла
5) Возможность закрытия открытой позиции заблаговременно, используя параметр Deviation
6) Код реализован на PineScript v4
Описание параметров:
Lot - % от вашего первоначального баланса, используется при расчете размера Ордера
Timeframe Short - таймфрейм, используемый для расчета МА Открытия Шорт позиций, может быть выбран из списка, по умолчанию - таймфрейм текущего графика
MA Type Short - тип MA, используемый для расчета МА Открытия Шорт позиций, может быть выбран из списка, по умолчанию SMA
Data Short - источник цены для расчета МА Открытия Шорт позиций, может быть выбран из списка, по умолчанию OHLC4
MA Length Short - период, используемый для расчета МА Открытия Шорт позиций, должен быть >= 1, по умолчанию 3
MA Offset Short - смещение значения MA, используемого для расчета Шорт Ордеров, должно быть >= 0, по умолчанию 0
Timeframe Long - таймфрейм, используемый для расчета МА Открытия Лонг позиций, может быть выбран из списка, по умолчанию - таймфрейм текущего графика
MA Type Long - тип MA, используемый для расчета МА Открытия Лонг позиций, может быть выбран из списка, по умолчанию SMA
Data Long - источник цены для расчета МА Открытия Лонг позиций, может быть выбран из списка, по умолчанию OHLC4
MA Length Long - период, используемый для расчета МА Открытия Лонг позиций, должен быть >= 1, по умолчанию 3
MA Offset Long - смещение значения MA, используемого для расчета Лонг Ордеров, должно быть >= 0, по умолчанию 0
Mode close MA Short - Включает отдельное MA для закрытия Шорт позиции, по умолчанию «false». Если false, MA Закрытия = MA Открытия
Timeframe Short Close - таймфрейм, используемый для расчета МА Закрытия Шорт позиций, может быть выбран из списка, по умолчанию - таймфрейм текущего графика
MA Type Close Short - тип MA, используемый при расчете МА Закрытия Шорт позиции. Mожно выбрать из списка, по умолчанию SMA
Data Short Close - источник цены для расчета МА Закрытия Шорт позиций, может быть выбран из списка, по умолчанию OHLC4
MA Length Short Close - период, используемый для расчета МА Закрытия Шорт позиции, должен быть >= 1, по умолчанию 3
Short Deviation - % отклонения от значения MA, используется для закрытия позиции выше или ниже рассчитанного значения MA, может быть отрицательным, по умолчанию 0
MA Offset Short Close - смещение значения MA, используемого для расчета закрытия Шорт позиции, должно быть >= 0, по умолчанию 0
Mode close MA Long - Включает разные MA для закрытия Лонг позиции, по умолчанию «false». Если false, MA Закрытия = MA Открытия
Timeframe Long Close - таймфрейм, используемый для расчета МА Закрытия Лонг позиций, может быть выбран из списка, по умолчанию - таймфрейм текущего графика
MA Type Close Long - тип MA, используемый при расчете МА Закрытия Лонг позиции. Mожно выбрать из списка, по умолчанию SMA
Data Long Close - источник цены для расчета МА Закрытия Лонг позиций, может быть выбран из списка, по умолчанию OHLC4
MA Length Long Close - период, используемый для расчета МА Закрытия Лонг позиции, должен быть >= 1, по умолчанию 3
Long Deviation -% для перехода от значения MA, используется для закрытия позиции выше или ниже рассчитанного значения MA, может быть отрицательным, по умолчанию 0
MA Offset Long Close - смещение значения MA, используемого для расчета закрытия Лонг позиции, должно быть >= 0, по умолчанию 0
Short Shift 1..4 - % от значения MA для размещения Ордеров, для Шорт Ордеров должен быть положительным, чем выше номер, тем выше должна располагаться позиция Shift, например: «Shift 1 = 1, Shift 2 = 2 и т.д. "
Long Shift 1..4 - % от значения MA для размещения Ордеров, для Лонг Ордеров должно быть отрицательным, чем ниже число, тем ниже должна располагаться позиция Shift, например: «Shift 1 = -1, Shift 2 = -2, и т.д."
From Year 20XX - Год начала тестирования, из-за ориентированности на криптовалюты поддерживаются только значения формата 20хх.
To Year 20XX - Год окончания тестирования, из-за ориентированности на криптовалюты поддерживаются только значения формата 20хх.
From Month - Начальный месяц, опционально, менять не рекомендуется
To Month - Конечный месяц, опционально, менять не рекомендуется
From day - Начальный день месяца, опционально, менять не рекомендуется
To day - Конечный день месяца, опционально, менять не рекомендуется
Пояснения к графику:
Зеленые линии - Лонг Ордера.
Красные линии - Шорт Ордера.
Оранжевая линия - MA Закрытия Шорт позиций.
Лаймовая линия - MA Закрытия Лонг позиций.
SMA Cross Entry & Exit StrategyThis is a TradingView Strategy Script meaning you can't execute real trades using your exchange API connected to your TradingView account, it is designed for backtesting only
This is a basic backtesting script for charting the bullish and bearish cross of two user defined simple moving averages, select the cog next to the name of the script ON the price chart in the left hand corner. The script will print to the screen either "Long Entry" or "Short Entry" depending on the direction of the cross. The script using TradingView strategies will subsequently close the opposite of the position that is executed when the bullish or bearish cross occurs. Simply put, if you are short and a bullish cross occurs, your short trade will close and be logged in strategies and the long will fire. You can pyramid the long and short positions to continue entering as long as the trend doesn't flip. You will find this in the script settings. Since this script is for backtesting you can manually set the "backtesting range" for TradingView Strategies and firing the "Long Entry" and "Short Entry". This as well, is in the settings.
Notice: When the SMA cross occurs, you have to wait till the next candle before TradingView Strategy will print the "Long Entry" or "Short Entry" to the screen
TradingView - How To Use Strategies: www.tradingview.com
Noro's SILA v1.6L StrategyBacktesting
Backtesting (for all the time of existence of couple) only with software configurations to default (without optimization of parameters):
US = Uptrend-Sensivity
DS = Downtrend-Sensivity
It is recommended and by default:
- the normal market requires US=DS (for example US=5, DS=5)
- very bear market requires US DS, (for example US=5, DS=0)
- very bull market requires US DS, (US=0, DS=5)
Cryptocurrencies it is very bull market (US=0, DS=5)
Backtesting BTC/FIAT
D1 timeframe
identical parameters for all pairs
BTC/USD (Bitstamp) profit of +41805%
BTC/EUR (BTC-e) profit of +1147%
BTC/RUB (BTC-e) profit of +1162%
BTC/JPY (Bitflyer) profit of +215%
BTC/CNY (BTCChina) profit of 54948%
Backtesting ALTCOIN/BTC
D1 timeframe
identical parameters for all pairs
the exchange Poloniex
top-10 of cryptocurrencies on capitalization at the time of this text
NA = TradingView can't make backtest because of too low price of this cryptocurrency, or on the website there are no quotations of this cryptocurrency
ETH/BTC (Etherium) profit of +11690%
XRP/BTC (Ripple) loss of-100%
LTC/BTC (Litecoin) NA
ETC/BTC (Etherium Classic) profit of +214%
NEM/BTC loss of-49%
DASH/BTC profit of +106%
IOTA/BTC NA
XMR/BTC (Monero) profit of +96%
STRAT/BTC (Stratis) loss of-31%
ALTCOIN/ALTCOIN - not recomended
I don't need your money, I need reputation and likes.
Live Market - Performance MonitorLive Market — Performance Monitor
Study material (no code) — step-by-step training guide for learners
________________________________________
1) What this tool is — short overview
This indicator is a live market performance monitor designed for learning. It scans price, volume and volatility, detects order blocks and trendline events, applies filters (volume & ATR), generates trade signals (BUY/SELL), creates simple TP/SL trade management, and renders a compact dashboard summarizing market state, risk and performance metrics.
Use it to learn how multi-factor signals are constructed, how Greeks-style sensitivity is replaced by volatility/ATR reasoning, and how a live dashboard helps monitor trade quality.
________________________________________
2) Quick start — how a learner uses it (step-by-step)
1. Add the indicator to a chart (any ticker / timeframe).
2. Open inputs and review the main groups: Order Block, Trendline, Signal Filters, Display.
3. Start with defaults (OB periods ≈ 7, ATR multiplier 0.5, volume threshold 1.2) and observe the dashboard on the last bar.
4. Walk the chart back in time (use the last-bar update behavior) and watch how signals, order blocks, trendlines, and the performance counters change.
5. Run the hands-on labs below to build intuition.
________________________________________
3) Main configurable inputs (what you can tweak)
• Order Block Relevant Periods (default ~7): number of consecutive candles used to define an order block.
• Min. Percent Move for Valid OB (threshold): minimum percent move required for a valid order block.
• Number of OB Channels: how many past order block lines to keep visible.
• Trendline Period (tl_period): pivot lookback for detecting highs/lows used to draw trendlines.
• Use Wicks for Trendlines: whether pivot uses wicks or body.
• Extension Bars: how far trendlines are projected forward.
• Use Volume Filter + Volume Threshold Multiplier (e.g., 1.2): requires volume to be greater than multiplier × average volume.
• Use ATR Filter + ATR Multiplier: require bar range > ATR × multiplier to filter noise.
• Show Targets / Table settings / Colors for visualization.
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4) Core building blocks — what the script computes (plain language)
Price & trend:
• Spot / LTP: current close price.
• EMA 9 / 21 / 50: fast, medium, slow moving averages to define short/medium trend.
o trend_bullish: EMA9 > EMA21 > EMA50
o trend_bearish: EMA9 < EMA21 < EMA50
o trend_neutral: otherwise
Volatility & noise:
• ATR (14): average true range used for dynamic target and filter sizing.
• dynamic_zone = ATR × atr_multiplier: minimum bar range required for meaningful move.
• Annualized volatility: stdev of price changes × sqrt(252) × 100 — used to classify volatility (HIGH/MEDIUM/LOW).
Momentum & oscillators:
• RSI 14: overbought/oversold indicator (thresholds 70/30).
• MACD: EMA(12)-EMA(26) and a 9-period signal line; histogram used for momentum direction and strength.
• Momentum (ta.mom 10): raw momentum over 10 bars.
Mean reversion / band context:
• Bollinger Bands (20, 2σ): upper, mid, lower.
o price_position measures where price sits inside the band range as 0–100.
Volume metrics:
• avg_volume = SMA(volume, 20) and volume_spike = volume > avg_volume × volume_threshold
o volume_ratio = volume / avg_volume
Support & Resistance:
• support_level = lowest low over 20 bars
• resistance_level = highest high over 20 bars
• current_position = percent of price between support & resistance (0–100)
________________________________________
5) Order Block detection — concept & logic
What it tries to find: a bar (the base) followed by N candles in the opposite direction (a classical order block setup), with a minimum % move to qualify. The script records the high/low of the base candle, averages them, and plots those levels as OB channels.
How learners should think about it (conceptual):
1. An order block is a signature area where institutions (theory) left liquidity — often seen as a large bar followed by a sequence of directional candles.
2. This indicator uses a configurable number of subsequent candles to confirm that the pattern exists.
3. When found, it stores and displays the base candle’s high/low area so students can see how price later reacts to those zones.
Implementation note for learners: the tool keeps a limited history of OB lines (ob_channels). When new OBs exceed the count, the oldest lines are removed — good practice to avoid clutter.
________________________________________
6) Trendline detection — idea & interpretation
• The script finds pivot highs and lows using a symmetric lookback (tl_period and half that as right/left).
• It then computes a trendline slope from successive pivots and projects the line forward (extension_bars).
• Break detection: Resistance break = close crosses above the projected resistance line; Support break = close crosses below projected support.
Learning tip: trendlines here are computed from pivot points and time. Watch how changing tl_period (bigger = smoother, fewer pivots) alters the trendlines and break signals.
________________________________________
7) Signal generation & filters — step-by-step
1. Primary triggers:
o Bullish trigger: order block bullish OR resistance trendline break.
o Bearish trigger: bearish order block OR support trendline break.
2. Filters applied (both must pass unless disabled):
o Volume filter: volume must be > avg_volume × volume_threshold.
o ATR filter: bar range (high-low) must exceed ATR × atr_multiplier.
o Not in an existing trade: new trades only start if trade_active is false.
3. Trend confirmation:
o The primary trigger is only confirmed if trend is bullish/neutral for buys or bearish/neutral for sells (EMA alignment).
4. Result:
o When confirmed, a long or short trade is activated with TP/SL calculated from ATR multiples.
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8) Trade management — what the tool does after a signal
• Entry management: the script marks a trade as trade_active and sets long_trade or short_trade flags.
• TP & SL rules:
o Long: TP = high + 2×ATR ; SL = low − 1×ATR
o Short: TP = low − 2×ATR ; SL = high + 1×ATR
• Monitoring & exit:
o A trade closes when price reaches TP or SL.
o When TP/SL hit, the indicator updates win_count and total_pnl using a very simple calculation (difference between TP/SL and previous close).
o Visual lines/labels are drawn for TP and updated as the trade runs.
Important learner notes:
• The script does not store a true entry price (it uses close in its P&L math), so PnL is an approximation — treat this as a learning proxy, not a position accounting system.
• There’s no sizing, slippage, or fee accounted — students must manually factor these when translating to real trades.
• This indicator is not a backtesting strategy; strategy.* functions would be needed for rigorous backtest results.
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9) Signal strength & helper utilities
• Signal strength is a composite score (0–100) made up of four signals worth 25 points each:
1. RSI extreme (overbought/oversold) → 25
2. Volume spike → 25
3. MACD histogram magnitude increasing → 25
4. Trend existence (bull or bear) → 25
• Progress bars (text glyphs) are used to visually show RSI and signal strength on the table.
Learning point: composite scoring is a way to combine orthogonal signals — study how changing weights changes outcomes.
________________________________________
10) Dashboard — how to read each section (walkthrough)
The dashboard is split into sections; here's how to interpret them:
1. Market Overview
o LTP / Change%: immediate price & daily % change.
2. RSI & MACD
o RSI value plus progress bar (overbought 70 / oversold 30).
o MACD histogram sign indicates bullish/bearish momentum.
3. Volume Analysis
o Volume ratio (current / average) and whether there’s a spike.
4. Order Block Status
o Buy OB / Sell OB: the average base price of detected order blocks or “No Signal.”
5. Signal Status
o 🔼 BUY or 🔽 SELL if confirmed, or ⚪ WAIT.
o No-trade vs Active indicator summarizing market readiness.
6. Trend Analysis
o Trend direction (from EMAs), market sentiment score (composite), volatility level and band/position metrics.
7. Performance
o Win Rate = wins / signals (percentage)
o Total PnL = cumulative PnL (approximate)
o Bull / Bear Volume = accumulated volumes attributable to signals
8. Support & Resistance
o 20-bar highest/lowest — use as nearby reference points.
9. Risk & R:R
o Risk Level from ATR/price as a percent.
o R:R Ratio computed from TP/SL if a trade is active.
10. Signal Strength & Active Trade Status
• Numeric strength + progress bar and whether a trade is currently active with TP/SL display.
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11) Alerts — what will notify you
The indicator includes pre-built alert triggers for:
• Bullish confirmed signal
• Bearish confirmed signal
• TP hit (long/short)
• SL hit (long/short)
• No-trade zone
• High signal strength (score > 75%)
Training use: enable alerts during a replay session to be notified when the indicator would have signalled.
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12) Labs — hands-on exercises for learners (step-by-step)
Lab A — Order Block recognition
1. Pick a 15–30 minute timeframe on a liquid ticker.
2. Use default OB periods (7). Mark each time the dashboard shows a Buy/Sell OB.
3. Manually inspect the chart at the base candle and the following sequence — draw the OB zone by hand and watch later price reactions to it.
4. Repeat with OB periods 5 and 10; note stability vs noise.
Lab B — Trendline break confirmation
1. Increase trendline period (e.g., 20), watch trendlines form from pivots.
2. When a resistance break is flagged, compare with MACD & volume: was momentum aligned?
3. Note false breaks vs confirmed moves — change extension_bars to see projection effects.
Lab C — Filter sensitivity
1. Toggle Use Volume Filter off, and record the number and quality of signals in a 2-day window.
2. Re-enable volume filter and change threshold from 1.2 → 1.6; note how many low-quality signals are filtered out.
Lab D — Trade management simulation
1. For each signalled trade, record the time, close entry approximation, TP, SL, and eventual hit/miss.
2. Compute actual PnL if you had entered at the open of the next bar to compare with the script’s PnL math.
3. Tabulate win rate and average R:R.
Lab E — Performance review & improvement
1. Build a spreadsheet of signals over 30–90 periods with columns: Date, Signal type, Entry price (real), TP, SL, Exit, PnL, Notes.
2. Analyze which filters or indicators contributed most to winners vs losers and adjust weights.
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13) Common pitfalls, assumptions & implementation notes (things to watch)
• P&L simplification: total_pnl uses close as a proxy entry price. Real entry/exit prices and slippage are not recorded — so PnL is approximate.
• No position sizing or money management: the script doesn’t compute position size from equity or risk percent.
• Signal confirmation logic: composite "signal_strength" is a simple 4×25 point scheme — explore different weights or additional signals.
• Order block detection nuance: the script defines the base candle and checks the subsequent sequence. Be sure to verify whether the intended candle direction (base being bullish vs bearish) aligns with academic/your trading definition — read the code carefully and test.
• Trendline slope over time: slope is computed using timestamps; small differences may make lines sensitive on very short timeframes — using bar_index differences is usually more stable.
• Not a true backtester: to evaluate performance statistically you must transform the logic into a strategy script that places hypothetical orders and records exact entry/exit prices.
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14) Suggested improvements for advanced learners
• Record true entry price & timestamp for accurate PnL.
• Add position sizing: risk % per trade using SL distance and account size.
• Convert to strategy. (Pine Strategy)* to run formal backtests with equity curves, drawdowns, and metrics (Sharpe, Sortino).
• Log trades to an external spreadsheet (via alerts + webhook) for offline analysis.
• Add statistics: average win/loss, expectancy, max drawdown.
• Add additional filters: news time blackout, market session filters, multi-timeframe confirmation.
• Improve OB detection: combine wick/body, volume spike at base bar, and liquidity sweep detection.
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15) Glossary — quick definitions
• ATR (Average True Range): measure of typical range; used to size targets and stops.
• EMA (Exponential Moving Average): trend smoothing giving more weight to recent prices.
• RSI (Relative Strength Index): momentum oscillator; >70 overbought, <30 oversold.
• MACD: momentum oscillator using difference of two EMAs.
• Bollinger Bands: volatility bands around SMA.
• Order Block: a base candle area with subsequent confirmation candles; a zone of institutional interest (learning model).
• Pivot High/Low: local turning point defined by candles on both sides.
• Signal Strength: combined score from multiple indicators.
• Win Rate: proportion of signals that hit TP vs total signals.
• R:R (Risk:Reward): ratio of potential reward (TP distance) to risk (entry to SL).
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16) Limitations & assumptions (be explicit)
• This is an indicator for learning — not a trading robot or broker connection.
• No slippage, fees, commissions or tie-in to real orders are considered.
• The logic is heuristic (rule-of-thumb), not a guarantee of performance.
• Results are sensitive to timeframe, market liquidity, and parameter choices.
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17) Practical classroom / study plan (4 sessions)
• Session 1 — Foundations: Understand EMAs, ATR, RSI, MACD, Bollinger Bands. Run the indicator and watch how these numbers change on a single day.
• Session 2 — Zones & Filters: Study order blocks and trendlines. Test volume & ATR filters and note changes in false signals.
• Session 3 — Simulated trading: Manually track 20 signals, compute real PnL and compare to the dashboard.
• Session 4 — Improvement plan: Propose changes (e.g., better PnL accounting, alternative OB rule) and test their impact.
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18) Quick reference checklist for each signal
1. Was an order block or trendline break detected? (primary trigger)
2. Did volume meet threshold? (filter)
3. Did ATR filter (bar size) show a real move? (filter)
4. Was trend aligned (EMA 9/21/50)? (confirmation)
5. Signal confirmed → mark entry approximation, TP, SL.
6. Monitor dashboard (Signal Strength, Volatility, No-trade zone, R:R).
7. After exit, log real entry/exit, compute actual PnL, update spreadsheet.
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19) Educational caveat & final note
This tool is built for training and analysis: it helps you see how common technical building blocks combine into trade ideas, but it is not a trading recommendation. Use it to develop judgment, to test hypotheses, and to design robust systems with proper backtesting and risk control before risking capital.
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20) Disclaimer (must include)
Training & Educational Only — This material and the indicator are provided for educational purposes only. Nothing here is investment advice or a solicitation to buy or sell financial instruments. Past simulated or historical performance does not predict future results. Always perform full backtesting and risk management, and consider seeking advice from a qualified financial professional before trading with real capital.
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Infinite EMA with Alpha Control♾️ Infinite EMA with Alpha Control
What Makes This EMA "Infinite"?
Unlike traditional EMA indicators that are limited to typical periods (1-5000), this Infinite EMA breaks all boundaries. You can create EMAs with periods of 1,000, 10,000, or even 1,000,000 bars - that's why it's called "infinite"! Also Infinite EMA starts working immediately from the very first bar on your chart
Why This EMA is "Infinite":
1. Mathematically: When N → ∞, alpha → 0, meaning infinitely long "memory"
2. Practically: You can set any period - even 100,000 bars
3. Flexibility: Alpha allows precise control over the "forgetting speed"
How Does It Work?
The magic lies in the Alpha parameter. While regular EMAs use fixed formulas, this indicator gives you direct control over the EMA's "memory" through Alpha values:
• High Alpha (0.1-0.2): Fast reaction, short memory
• Medium Alpha (0.01-0.05): Balanced response
• Low Alpha (0.0001-0.001): Extremely slow reaction, very long memory
• Ultra-low Alpha (0.000001): Almost frozen in time
The Mathematical Formula:
Alpha = 2 / (Period + 1)
This means you can achieve any EMA period by adjusting Alpha, giving you infinite flexibility!
Expanded "Infinite EMA" Table:
Period EMA (N) - Alpha (Rounded) - Alpha (Exact) - Description
10 - 0.1818 - 0.181818... - Fast EMA
20 - 0.0952 - 0.095238... - Short-term
50 - 0.0392 - 0.039215... - Medium-term
100 - 0.0198 - 0.019801... - Long-term
200 - 0.0100 - 0.009950... - Standard long-term
500 - 0.0040 - 0.003996... - Very long-term
1,000 - 0.0020 - 0.001998... - Super long-term
2,000 - 0.0010 - 0.000999... - Ultra long-term
5,000 - 0.0004 - 0.000399... - Mega long-term
10,000 - 0.0002 - 0.000199... - Giga long-term
25,000 - 0.00008 - 0.000079... - Century-scale EMA
50,000 - 0.00004 - 0.000039... - Practically motionless
100,000 - 0.00002 - 0.000019... - "Glacial" EMA
500,000 - 0.000004 - 0.000003... - Geological timescale
1,000,000 - 0.000002 - 0.000001... - Approaching constant
5,000,000 - 0.0000004 - 0.0000003... - Virtually static
10,000,000 - 0.0000002 - 0.0000001... - Nearly flat line
100,000,000 - 0.00000002 - 0.00000001... - Mathematical infinity
Formula: Alpha = 2/(N+1) where N is the EMA period
Key Features:
Dual EMA System: Run fast and slow EMAs simultaneously
Crossover Signals: Automatic buy/sell signals with customizable alerts
Alpha Control: Direct mathematical control over EMA behavior
Infinite Periods: From 1 to 100,000,000+ bars
Visual Customization: Colors, fills, backgrounds, signal sizes
Instant Start: Works accurately from the very first bar
Update Intervals: Control calculation frequency for noise reduction
Why Choose Infinite EMA?
1. Unlimited Flexibility: Any period you can imagine
2. Mathematical Precision: Direct alpha control for exact behavior
3. Professional Grade: Suitable for all trading styles
4. Easy to Use: Simple settings with powerful results
5. No Warm-up Period: Accurate values from bar #1
Simple Explanation:
Think of EMA as a "memory system":
• High Alpha = Short memory (forgets quickly, reacts fast)
• Low Alpha = Long memory (remembers everything, moves slowly)
With Infinite EMA, you can set the "memory length" to anything from seconds to centuries!
⚡ Instant Start Feature - EMA from First Bar
Immediate Calculation from Bar #1
Unlike traditional EMA indicators that require a "warm-up period" of N bars before showing accurate values, Infinite EMA starts working immediately from the very first bar on your chart.
How It Works:
Traditional EMA Problem:
• Standard 200-period EMA: Needs 200+ bars to become accurate
• First 200 bars: Shows incorrect/unstable values
• Result: Large portions of historical data are unusable
Infinite EMA Solution:
Bar #1: EMA = Current Price (perfect starting point)
Bar #2: EMA = Alpha × Price + (1-Alpha) × Previous EMA
Bar #3: EMA = Alpha × Price + (1-Alpha) × Previous EMA
...and so on
Key Benefits:
No Warm-up Period: Start trading signals from day one
Full Chart Coverage: Every bar has a valid EMA value
Historical Accuracy: Backtesting works on entire dataset
New Markets: Works perfectly on newly listed assets
Short Datasets: Effective even with limited historical data
Practical Impact:
Scenario Traditional EMA Infinite EMA
New cryptocurrency Unusable for first 200 days ✅ Works from day 1
Limited data (< 200 bars) Inaccurate values ✅ Fully functional
Backtesting Must skip first 200 bars ✅ Test entire history
Real-time trading Wait for stabilization ✅ Trade immediately
Technical Implementation:
if barstate.isfirst
EMA := currentPrice // Perfect initialization
else
EMA := alpha × currentPrice + (1-alpha) × previousEMA
This smart initialization ensures mathematical accuracy from the very first calculation, eliminating the traditional EMA "ramp-up" problem.
Why This Matters:
For Backesters: Use 100% of available data
For Live Trading: Get signals immediately on any timeframe
For Researchers: Analyze complete datasets without gaps
Bottom Line: Infinite EMA is ready to work the moment you add it to your chart - no waiting, no warm-up, no exceptions!
Unlike traditional EMAs that require a "warm-up period" of 200+ bars before showing accurate values, Infinite EMA starts working immediately from bar #1.
This breakthrough eliminates the common problem where the first portion of your chart shows unreliable EMA data. Whether you're analyzing a newly listed cryptocurrency, working with limited historical data, or backtesting strategies, every single bar provides mathematically accurate EMA values.
No more waiting periods, no more unusable data sections - just instant, reliable trend analysis from the moment you apply the indicator to any chart.
🔄 Update Interval Bars Feature
The Update Interval feature allows you to control how frequently the EMA recalculates, providing flexible noise filtering without changing the core mathematics.
Set to 1 for standard behavior (updates every bar), or increase to 5-10 for smoother signals that update less frequently. Higher intervals reduce market noise and false signals but introduce slightly more lag. This is particularly useful on volatile timeframes where you want the EMA's directional bias without every minor price fluctuation affecting the calculation.
Perfect for swing traders who prefer cleaner, more stable trend lines over hyper-responsive indicators.
Conclusion
The Infinite EMA transforms the traditional EMA from a fixed-period tool into a precision instrument with unlimited flexibility. By understanding the Alpha-Period relationship, traders can create custom EMAs that perfectly match their trading style, timeframe, and market conditions.
The "infinite" nature comes from the ability to set any period imaginable - from ultra-fast 2-bar EMAs to glacially slow 10-million-bar EMAs, all controlled through a single Alpha parameter.
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Whether you're a beginner looking for simple trend following or a professional researcher analyzing century-long patterns, Infinite EMA adapts to your needs. The power of infinite periods is now in your hands! 🚀
Go forward to the horizon. When you reach it, a new one will open up.
- J. P. Morgan
Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING
Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003).
Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices.
The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations.
Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005).
LITERATURE REVIEW AND THEORETICAL FOUNDATION
The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis.
Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches.
The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches.
Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns.
METHODOLOGY
The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as:
S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t)
where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term.
Technical Analysis Component
The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics.
Fundamental Analysis Framework
The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors.
Sector Classification System
Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations.
Macroeconomic Component
The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis.
Dynamic Threshold Adjustment
Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999).
The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning.
During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994).
USER GUIDE AND IMPLEMENTATION FRAMEWORK
Initial Setup and Configuration
The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles.
Display Configuration Settings
The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility.
Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008).
Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings.
Practical Example: Conservative Investor Setup
For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns.
Threshold Adaptation System Configuration
The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing.
Static Mode Implementation
Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons.
Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture.
Regime-Based Adaptation
Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments.
Technical Implementation:
The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress.
For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification.
VIX-Based Contrarian System
The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles.
Scientific Calibration:
VIX levels are calibrated according to historical percentile distributions:
Extreme High (>40):
- Maximum contrarian opportunity
- Threshold reduction: 15-20 points
- Historical accuracy: 85%+
High (30-40):
- Significant contrarian potential
- Threshold reduction: 10-15 points
- Market stress indicator
Medium (25-30):
- Moderate adjustment
- Threshold reduction: 5-10 points
- Normal volatility range
Low (15-25):
- Minimal adjustment
- Standard threshold levels
- Complacency monitoring
Extreme Low (<15):
- Counter-contrarian positioning
- Threshold increase: 5-10 points
- Bubble warning signals
Practical Example: VIX-Based Implementation for Active Traders
High Fear Environment (VIX >35):
- Thresholds decrease by 10-15 points
- Enhanced contrarian positioning
- Crisis opportunity capture
Low Fear Environment (VIX <15):
- Thresholds increase by 8-15 points
- Reduced signal frequency
- Bubble risk management
Additional Macro Factors:
- Yield curve considerations
- Dollar strength impact
- Global volatility spillover
Hybrid Mode Optimization
Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models.
Weighting Scheme:
- Regime factors: 40%
- VIX factors: 40%
- Additional macro considerations: 20%
Dynamic Calculation:
Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2)
Benefits:
- Balanced approach
- Reduced single-factor dependency
- Enhanced robustness
Advanced Mode with Stress Weighting
Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators:
Stress Level Indicators:
1. Yield curve inversion (recession predictor)
2. Volatility spikes (market disruption)
3. Severe drawdowns (momentum breaks)
4. VIX extreme readings (sentiment extremes)
Technical Implementation:
Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors:
Low Stress (0-1 factors):
- Regime weighting: 50%
- VIX weighting: 30%
- Macro weighting: 20%
Medium Stress (2 factors):
- Regime weighting: 40%
- VIX weighting: 40%
- Macro weighting: 20%
High Stress (3-4 factors):
- Regime weighting: 20%
- VIX weighting: 50%
- Macro weighting: 30%
Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007).
Percentile-Based Historical Analysis
Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978).
Methodology:
- Analyzes trailing 252-day periods (approximately 1 trading year)
- Establishes percentile-based thresholds
- Dynamic adaptation to market conditions
- Statistical significance testing
Configuration Options:
- Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable)
- Percentile Levels: Customizable based on signal frequency preferences
- Update Frequency: Daily recalculation with rolling windows
Implementation Example:
- Strong Buy Threshold: 75th percentile of historical scores
- Caution Buy Threshold: 60th percentile of historical scores
- Dynamic adjustment based on current market volatility
Investor Psychology Profile Configuration
The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research.
Conservative Profile Implementation
Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities.
Technical Calibration:
VIX Parameters:
- Extreme High Threshold: 32.0 (lower sensitivity to fear spikes)
- High Threshold: 28.0
- Adjustment Magnitude: Reduced for stability
Regime Adjustments:
- Bear Market Reduction: -7 points (vs -12 for normal)
- Recession Reduction: -10 points (vs -15 for normal)
- Conservative approach to crisis opportunities
Percentile Requirements:
- Strong Buy: 80th percentile (higher selectivity)
- Caution Buy: 65th percentile
- Signal frequency: Reduced for quality focus
Risk Management:
- Enhanced bankruptcy screening
- Stricter liquidity requirements
- Maximum leverage limits
Practical Application: Conservative Profile for Retirement Portfolios
This configuration suits investors requiring capital preservation with moderate growth:
- Reduced drawdown probability
- Research-based parameter selection
- Emphasis on fundamental safety
- Long-term wealth preservation focus
Normal Profile Optimization
Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices.
Calibration Parameters:
VIX Thresholds:
- Extreme High: 35.0 (institutional standard)
- High: 30.0
- Standard adjustment magnitude
Regime Adjustments:
- Bear Market: -12 points (moderate contrarian approach)
- Recession: -15 points (crisis opportunity capture)
- Balanced risk-return optimization
Percentile Requirements:
- Strong Buy: 75th percentile (industry standard)
- Caution Buy: 60th percentile
- Optimal signal frequency
Risk Management:
- Standard institutional practices
- Balanced screening criteria
- Moderate leverage tolerance
Aggressive Profile for Active Management
Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999).
Technical Configuration:
VIX Parameters:
- Extreme High: 40.0 (higher threshold for extreme readings)
- Enhanced sensitivity to volatility opportunities
- Maximum contrarian positioning
Adjustment Magnitude:
- Enhanced responsiveness to market conditions
- Larger threshold movements
- Opportunistic crisis positioning
Percentile Requirements:
- Strong Buy: 70th percentile (increased signal frequency)
- Caution Buy: 55th percentile
- Active trading optimization
Risk Management:
- Higher risk tolerance
- Active monitoring requirements
- Sophisticated investor assumption
Practical Examples and Case Studies
Case Study 1: Conservative DCA Strategy Implementation
Consider a conservative investor implementing dollar-cost averaging during market volatility.
AITM Configuration:
- Threshold Mode: Hybrid
- Investor Profile: Conservative
- Sector Adaptation: Enabled
- Macro Integration: Enabled
Market Scenario: March 2020 COVID-19 Market Decline
Market Conditions:
- VIX reading: 82 (extreme high)
- Yield curve: Steep (recession fears)
- Market regime: Bear
- Dollar strength: Elevated
Threshold Calculation:
- Base threshold: 75% (Strong Buy)
- VIX adjustment: -15 points (extreme fear)
- Regime adjustment: -7 points (conservative bear market)
- Final threshold: 53%
Investment Signal:
- Score achieved: 58%
- Signal generated: Strong Buy
- Timing: March 23, 2020 (market bottom +/- 3 days)
Result Analysis:
Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery.
Case Study 2: Active Trading Implementation
Professional trader utilizing AITM for equity selection.
Configuration:
- Threshold Mode: Advanced
- Investor Profile: Aggressive
- Signal Labels: Enabled
- Macro Data: Full integration
Analysis Process:
Step 1: Sector Classification
- Company identified as technology sector
- Enhanced growth weighting applied
- R&D intensity adjustment: +5%
Step 2: Macro Environment Assessment
- Stress level calculation: 2 (moderate)
- VIX level: 28 (moderate high)
- Yield curve: Normal
- Dollar strength: Neutral
Step 3: Dynamic Weighting Calculation
- VIX weighting: 40%
- Regime weighting: 40%
- Macro weighting: 20%
Step 4: Threshold Calculation
- Base threshold: 75%
- Stress adjustment: -12 points
- Final threshold: 63%
Step 5: Score Analysis
- Technical score: 78% (oversold RSI, volume spike)
- Fundamental score: 52% (growth premium but high valuation)
- Macro adjustment: +8% (contrarian VIX opportunity)
- Overall score: 65%
Signal Generation:
Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period.
Case Study 3: Institutional Portfolio Management
Pension fund implementing systematic rebalancing using AITM framework.
Implementation Framework:
- Threshold Mode: Percentile-Based
- Investor Profile: Normal
- Historical Lookback: 252 days
- Percentile Requirements: 75th/60th
Systematic Process:
Step 1: Historical Analysis
- 252-day rolling window analysis
- Score distribution calculation
- Percentile threshold establishment
Step 2: Current Assessment
- Strong Buy threshold: 78% (75th percentile of trailing year)
- Caution Buy threshold: 62% (60th percentile of trailing year)
- Current market volatility: Normal
Step 3: Signal Evaluation
- Current overall score: 79%
- Threshold comparison: Exceeds Strong Buy level
- Signal strength: High confidence
Step 4: Portfolio Implementation
- Position sizing: 2% allocation increase
- Risk budget impact: Within tolerance
- Diversification maintenance: Preserved
Result:
The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing.
Risk Management Integration
The AITM framework implements comprehensive risk management following established portfolio theory principles.
Bankruptcy Risk Filter
Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis:
Primary Screening Criteria:
- Z-Score threshold: <1.8 (high distress probability)
- Current Ratio threshold: <1.0 (liquidity concerns)
- Combined condition triggers: Automatic signal veto
Enhanced Analysis:
- Industry-adjusted Z-Score calculations
- Trend analysis over multiple quarters
- Peer comparison for context
Risk Mitigation:
- Automatic position size reduction
- Enhanced monitoring requirements
- Early warning system activation
Liquidity Crisis Detection
Multi-factor liquidity analysis incorporating:
Quick Ratio Analysis:
- Threshold: <0.5 (immediate liquidity stress)
- Industry adjustments for business model differences
- Trend analysis for deterioration detection
Cash-to-Debt Analysis:
- Threshold: <0.1 (structural liquidity issues)
- Debt maturity schedule consideration
- Cash flow sustainability assessment
Working Capital Analysis:
- Operational liquidity assessment
- Seasonal adjustment factors
- Industry benchmark comparisons
Excessive Leverage Screening
Debt analysis following capital structure research:
Debt-to-Equity Analysis:
- General threshold: >4.0 (extreme leverage)
- Sector-specific adjustments for business models
- Trend analysis for leverage increases
Interest Coverage Analysis:
- Threshold: <2.0 (servicing difficulties)
- Earnings quality assessment
- Forward-looking capability analysis
Sector Adjustments:
- REIT-appropriate leverage standards
- Financial institution regulatory requirements
- Utility sector regulated capital structures
Performance Optimization and Best Practices
Timeframe Selection
Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness.
Recommended Implementation:
Primary Analysis:
- Daily (1D) charts for optimal signal quality
- Complete fundamental data integration
- Full macro environment analysis
Secondary Confirmation:
- 4-hour timeframes for intraday confirmation
- Technical indicator validation
- Volume pattern analysis
Avoid for Timing Applications:
- Weekly/Monthly timeframes reduce responsiveness
- Quarterly analysis appropriate for fundamental trends only
- Annual data suitable for long-term research only
Data Quality Requirements
The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability.
Quality Standards:
Minimum Requirements:
- 2 years of complete financial data
- Current quarterly updates within 90 days
- Audited financial statements
Optimal Configuration:
- 5+ years for trend analysis
- Quarterly updates within 45 days
- Complete regulatory filings
Geographic Standards:
- Developed market reporting requirements
- International accounting standard compliance
- Regulatory oversight verification
Portfolio Integration Strategies
AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation.
Integration Approach:
Position Sizing:
- Signal strength correlation with allocation size
- Risk-adjusted position scaling
- Portfolio concentration limits
Risk Budgeting:
- Stress-test based allocation
- Scenario analysis integration
- Correlation impact assessment
Diversification Analysis:
- Portfolio correlation maintenance
- Sector exposure monitoring
- Geographic diversification preservation
Rebalancing Frequency:
- Signal-driven optimization
- Transaction cost consideration
- Tax efficiency optimization
Troubleshooting and Common Issues
Missing Fundamental Data
When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability.
Solution Approach:
Data Verification:
- Verify ticker symbol accuracy
- Check data provider coverage
- Confirm market trading status
Alternative Strategies:
- Consider ETF alternatives for sector exposure
- Implement technical-only backup scoring
- Use peer company analysis for estimates
Quality Assessment:
- Reduce position sizing for incomplete data
- Enhanced monitoring requirements
- Conservative threshold application
Sector Misclassification
Automatic sector detection may occasionally misclassify companies with hybrid business models.
Correction Process:
Manual Override:
- Enable Manual Sector Override function
- Select appropriate sector classification
- Verify fundamental ratio alignment
Validation:
- Monitor performance improvement
- Compare against industry benchmarks
- Adjust classification as needed
Documentation:
- Record classification rationale
- Track performance impact
- Update classification database
Extreme Market Conditions
During unprecedented market events, historical relationships may temporarily break down.
Adaptive Response:
Monitoring Enhancement:
- Increase signal monitoring frequency
- Implement additional confirmation requirements
- Enhanced risk management protocols
Position Management:
- Reduce position sizing during uncertainty
- Maintain higher cash reserves
- Implement stop-loss mechanisms
Framework Adaptation:
- Temporary parameter adjustments
- Enhanced fundamental screening
- Increased macro factor weighting
IMPLEMENTATION AND VALIDATION
The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers.
Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0.
Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios.
RESULTS AND PRACTICAL APPLICATIONS
The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism.
Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995).
The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999).
LIMITATIONS AND FUTURE RESEARCH
Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly.
The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes.
Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives.
CONCLUSION
The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions.
The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements.
The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance.
REFERENCES
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20(3), 651-707.
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152.
Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of Financial Economics, 37(1), 39-65.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Calmar, T. (1991). The Calmar ratio: A smoother tool. Futures, 20(1), 40.
Edwards, R. D., Magee, J., & Bassetti, W. H. C. (2018). Technical Analysis of Stock Trends. 11th ed. Boca Raton: CRC Press.
Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61.
Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. Journal of Portfolio Management, 31(3), 92-100.
Graham, B., & Dodd, D. L. (2008). Security Analysis. 6th ed. New York: McGraw-Hill Education.
Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management. 2nd ed. New York: McGraw-Hill.
Guidolin, M., & Timmermann, A. (2007). Asset allocation under multivariate regime switching. Journal of Economic Dynamics and Control, 31(11), 3503-3544.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.
Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. Journal of Finance, 49(5), 1541-1578.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton: Princeton University Press.
Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of Economic Perspectives, 17(1), 59-82.
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.
Penman, S. H. (2012). Financial Statement Analysis and Security Valuation. 5th ed. New York: McGraw-Hill Education.
Piotroski, J. D. (2000). Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, 38, 1-41.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442.
Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 21(1), 49-58.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven: Yale University Press.
Whaley, R. E. (1993). Derivatives on market volatility: Hedging tools long overdue. Journal of Derivatives, 1(1), 71-84.
Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research.
SuperTrend Strategy with Trend-Based Exits🟩 SuperTrend Strategy with Trend-Based Exits
This is a fully automated trend-following strategy based on the popular SuperTrend indicator, enhanced with a position sizing algorithm tied to stop-loss distance and dynamic entry/exit rules. The strategy is designed for futures trading with an emphasis on sustainable risk, realistic backtesting, and transparent logic.
🧠 Concept and Methodology
The strategy uses the SuperTrend indicator, which is derived from ATR (Average True Range) and is widely used to capture medium- to long-term market trends.
Key features:
✅ Entries are triggered only when the SuperTrend direction changes (trend reversal).
✅ Exits are performed using a dynamic stop-loss placed at the SuperTrend line.
✅ Position size is automatically calculated based on the trader’s fixed dollar risk per trade and the current distance to the stop-loss.
✅ Rounding logic is included to ensure quantity is valid for the exchange’s lot size.
This strategy does not use any take-profit or classic trailing stop — the position is only closed when the trend reverses or the stop is hit by touching the SuperTrend line.
⚙️ Default Parameters
ATR Length: 300
Factor: 7.5
Risk per trade: $90 (3% of the default $3,000 capital)
Lot step: 10
Commission: 0.05%
These default parameters are not universal. They were optimized specifically for STXUSDT swap at 15M timeframe at Bybit and may not produce viable results on other pairs and timeframes.
Users are encouraged to customize the settings according to specific asset’s volatility, timeframe and other characteristics.
❗ These default settings yield meaningful backtesting results on STXUSDT with a reasonable number of trades (105+) over 7-month period. If applied to other assets, results may vary significantly.
📈 Position Sizing Logic
The strategy uses a dynamic position sizing formula:
Pine Script®
position_size = floor((risk_per_trade / stop_loss_distance) / lot_step) * lot_step
This ensures the trader always risks a fixed dollar amount per trade and never exceeds a sustainable equity exposure (recommended 2% or less).
✅ Realism in Backtesting
To ensure realistic and non-misleading backtest results, this strategy includes:
— Slippage and commission settings matching average exchange conditions (commission = 0.05%, slippage 5 ticks).
— Position sizing based on stop-loss distance (not fixed contract quantity).*
— A fixed risk-per-trade model that adheres to responsible capital management principles.
— This is in compliance with TradingView's Script publishing rules and House Rules.
📌 How to Use
Apply the strategy to a clean chart (preferably 15M for STXUSDT by default).
If using another asset, adjust:
- ATR Length
- Factor
- Risk per trade
- Qty step (lot precision for the symbol)
Avoid using with other indicators unless you understand their purpose.
Use the Strategy Tester to evaluate performance and optimize parameters.
⚠️ Disclaimer
This is not financial advice. Always perform forward testing and assess risk before deploying any strategy on live capital. The strategy is designed for educational and experimental use.
Modular Range-Trading Strategy (V9.2)# 模块化震荡行情策略 (V9.2)
# Modular Range-Trading Strategy (V9.2)
## 策略简介 | Strategy Overview
该策略基于布林带 (Bollinger Bands)、RSI、MACD、ADX 等经典指标的组合，通过多逻辑模块化结构识别震荡区间的价格反转机会，支持多空双向操作，并在相同逻辑下允许智能加仓，适用于震荡市场的回测和研究。
This strategy combines classic indicators such as Bollinger Bands, RSI, MACD, and ADX to identify price reversal opportunities within ranging markets. It features a modular multi-logic structure, allowing both long and short trades with intelligent pyramiding under the same logic. It is designed for backtesting and research in range-bound conditions.
---
## 功能特点 | Key Features
- **多逻辑结构**：支持多套震荡逻辑（动能确认均值回归、布林带极限反转等）。
- **加仓与仓位互斥**：同逻辑下可智能加仓，不同逻辑间自动互斥，避免冲突。
- **回测可调时间范围**：可自定义回测起止时间，精准评估策略表现。
- **指标可视化**：布林带、RSI、MACD 及动态 ATR 止损线实时绘图。
- **K线收盘确认信号**：通过 `barstate.isconfirmed` 控制信号，避免未收盘的虚假信号。
- **Multi-logic structure**: Supports multiple range-trading logics (e.g., momentum-based mean reversion, Bollinger Band reversals).
- **Pyramiding with mutual exclusion**: Allows intelligent pyramiding within the same logic while preventing conflicts between different logics.
- **Adjustable backtesting range**: Customizable start and end dates for accurate performance evaluation.
- **Visual indicators**: Real-time plotting of Bollinger Bands, RSI, MACD, and dynamic ATR stop lines.
- **Close-bar confirmation**: Uses `barstate.isconfirmed` to avoid false signals before bar close.
---
## 使用说明 | Usage
1. 将该脚本添加到 TradingView 图表。
2. 在参数中设置回测时间段和指标参数。
3. 仅用于学习与策略研究，请勿直接用于实盘交易。
1. Add this script to your TradingView chart.
2. Configure backtesting dates and indicator parameters as needed.
3. For educational and research purposes only. **Not for live trading.**
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## ⚠️ 免责声明 | Disclaimer
本策略仅供学习和研究使用，不构成任何形式的投资建议。
作者不参与任何实盘交易、资金管理或收益分成，也不保证策略盈利能力。
严禁将本脚本用于任何非法集资、私募募资或与虚拟货币相关的金融违法活动。
使用本策略即表示您自行承担所有风险与法律责任。
This strategy is for educational and research purposes only and does not constitute investment advice.
The author does not participate in live trading, asset management, or profit sharing, nor guarantee profitability.
The use of this script in illegal fundraising, private placements, or cryptocurrency-related financial activities is strictly prohibited.
By using this strategy, you accept all risks and legal responsibilities.
---
Range Filter Strategy [Real Backtest]Range Filter Strategy - Real Backtesting
# Overview
Advanced Range Filter strategy designed for realistic backtesting with precise execution timing and comprehensive risk management. Built specifically for cryptocurrency markets with customizable parameters for different assets and timeframes.
Core Algorithm
Range Filter Technology:
- Smooth Average Range calculation using dual EMA filtering
- Dynamic range-based price filtering to identify trend direction
- Anti-noise filtering system to reduce false signals
- Directional momentum tracking with upward/downward counters
Key Features
Real-Time Execution (No Delay)
- Process orders on tick: Immediate execution without waiting for bar close
- Bar magnifier integration for intrabar precision
- Calculate on every tick for maximum responsiveness
- Standard OHLC bypass for enhanced accuracy
Realistic Price Simulation
- HL2 entry pricing (High+Low)/2 for realistic fills
- Configurable spread buffer simulation
- Random slippage generation (0 to max slippage)
- Market liquidity validation before entry
Advanced Signal Filtering
- Volume-based filtering with customizable ratio
- Optional signal confirmation system (1-3 bars)
- Anti-repetition logic to prevent duplicate signals
- Daily trade limit controls
Risk Management
- Fixed Risk:Reward ratios with precise point calculation
- Automatic stop loss and take profit execution
- Position size management
- Maximum daily trades limitation
Alert System
- Real-time alerts synchronized with strategy execution
- Multiple alert types: Setup, Entry, Exit, Status
- Customizable message formatting with price/time inclusion
- TradingView alert panel integration
Default Parameters
Optimized for BTC 5-minute charts:
- Sampling Period: 100
- Range Multiplier: 3.0
- Risk: 50 points
- Reward: 100 points (1:2 R:R)
- Spread Buffer: 2.0 points
- Max Slippage: 1.0 points
Signal Logic
Long Entry Conditions:
- Price above Range Filter line
- Upward momentum confirmed
- Volume requirements met (if enabled)
- Confirmation period completed (if enabled)
- Daily trade limit not exceeded
Short Entry Conditions:
- Price below Range Filter line
- Downward momentum confirmed
- Volume requirements met (if enabled)
- Confirmation period completed (if enabled)
- Daily trade limit not exceeded
Visual Elements
- Range Filter line with directional coloring
- Upper and lower target bands
- Entry signal markers
- Risk/Reward ratio boxes
- Real-time settings dashboard
Customization Options
Market Adaptation:
- Adjust Sampling Period for different timeframes
- Modify Range Multiplier for various volatility levels
- Configure spread/slippage for different brokers
- Set appropriate R:R ratios for trading style
Filtering Controls:
- Enable/disable volume filtering
- Adjust confirmation requirements
- Set daily trade limits
- Customize alert preferences
Performance Features
- Realistic backtesting results aligned with live trading
- Elimination of look-ahead bias
- Proper order execution simulation
- Comprehensive trade statistics
Alert Configuration
Alert Types Available:
- Entry signals with complete trade information
- Setup alerts for early preparation
- Exit notifications for position management
- Filter direction changes for market context
Message Format:
Symbol - Action | Price: XX.XX | Stop: XX.XX | Target: XX.XX | Time: HH:MM
Usage Recommendations
Optimal Settings:
- Bitcoin/Major Crypto: Default parameters
- Forex: Reduce sampling period to 50-70, multiplier to 2.0-2.5
- Stocks: Reduce sampling period to 30-50, multiplier to 1.0-1.8
- Gold: Sampling period 60-80, multiplier 1.5-2.0
TradingView Configuration:
- Recalculate: "On every tick"
- Orders: "Use bar magnifier"
- Data: Real-time feed recommended
Risk Disclaimer
This strategy is designed for educational and analytical purposes. Past performance does not guarantee future results. Always test thoroughly on paper trading before live implementation. Consider market conditions, broker execution, and personal risk tolerance when using any automated trading system.
Best Settings Found for Gold 15-Minute Timeframe
After extensive testing and optimization, these are the most effective settings I've discovered for trading Gold (XAUUSD) on the 15-minute timeframe:
Core Filter Settings:
Sampling Period: 100
Range Multiplier: 3.0
Professional Execution Engine:
Realistic Entry: Enabled (HL2)
Spread Buffer: 2 points
Dynamic Slippage: Enabled with max 1 point
Volume Filter: Enabled at 1.7x ratio
Signal Confirmation: Enabled with 1 bar confirmation
Risk Management:
Stop Loss: 50 points
Take Profit: 100 points (2:1 Risk-Reward)
Max Trades Per Day: 5
These settings provide an excellent balance between signal accuracy and realistic market execution. The volume filter at 1.7x ensures we only trade during periods of sufficient market activity, while the 1-bar confirmation helps filter out false signals. The spread buffer and slippage settings account for real trading costs, making backtest results more realistic and achievable in live trading.
Range Filter Strategy [Arabic Real Backtest]استراتيجية مرشح النطاق - اختبار واقعي
نظرة عامة
استراتيجية مرشح النطاق المتقدمة مصممة للاختبار الواقعي مع توقيت تنفيذ دقيق وإدارة مخاطر شاملة. تم بناؤها خصيصًا لأسواق العملات الرقمية مع معلمات قابلة للتخصيص لأصول وفترات زمنية مختلفة.
الخوارزمية الأساسية
تقنية مرشح النطاق:
* حساب متوسط النطاق السلس باستخدام فلترة مزدوجة للـ EMA
* فلترة أسعار استنادًا إلى النطاق الديناميكي لتحديد اتجاه الاتجاه
* نظام فلترة ضد الضوضاء لتقليل الإشارات الخاطئة
* تتبع الزخم الاتجاهي مع عدادات للأعلى/للأسفل
الميزات الرئيسية
**التنفيذ الفوري (بدون تأخير)**
* معالجة الأوامر عند كل نقطة: تنفيذ فوري دون انتظار إغلاق الشمعة
* تكامل مكبر الشمعة للحصول على دقة داخل الشمعة
* الحساب في كل نقطة لضمان الاستجابة القصوى
* تجاوز OHLC القياسي لزيادة الدقة
**محاكاة الأسعار الواقعية**
* تسعير الدخول باستخدام HL2 (High+Low)/2 لملء واقعي
* محاكاة للبُعد العازل للسعر القابل للتخصيص
* إنشاء انزلاق عشوائي (من 0 إلى الحد الأقصى للانزلاق)
* التحقق من سيولة السوق قبل الدخول
**فلترة الإشارات المتقدمة**
* فلترة استنادًا إلى الحجم مع نسبة قابلة للتخصيص
* نظام تأكيد الإشارة اختياري (من 1 إلى 3 شموع)
* منطق مضاد للتكرار لمنع الإشارات المكررة
* التحكم في حد التداول اليومي
**إدارة المخاطر**
* نسب ثابتة للمخاطرة: العائد مع حساب دقيق للنقاط
* تنفيذ وقف الخسارة وجني الأرباح تلقائيًا
* إدارة حجم المركز
* تحديد الحد الأقصى للصفقات اليومية
**نظام التنبيهات**
* تنبيهات فورية متزامنة مع تنفيذ الاستراتيجية
* أنواع متعددة من التنبيهات: إعداد، دخول، خروج، حالة
* تخصيص تنسيق الرسائل مع تضمين السعر/الوقت
* تكامل مع لوحة تنبيهات TradingView
المعلمات الافتراضية
محسن لرسوم بيانية لفترة 5 دقائق لبيتكوين:
* فترة العينة: 100
* معامل النطاق: 3.0
* المخاطرة: 50 نقطة
* المكافأة: 100 نقطة (نسبة 1:2)
* بُعد الانتشار: 2.0 نقطة
* الحد الأقصى للانزلاق: 1.0 نقطة
منطق الإشارة
**شروط الدخول الطويل:**
* السعر فوق خط مرشح النطاق
* تأكيد الزخم الصاعد
* تلبية متطلبات الحجم (إذا تم تمكينها)
* اكتمال فترة التأكيد (إذا تم تمكينها)
* لم يتم تجاوز حد الصفقات اليومية
**شروط الدخول القصير:**
* السعر تحت خط مرشح النطاق
* تأكيد الزخم الهابط
* تلبية متطلبات الحجم (إذا تم تمكينها)
* اكتمال فترة التأكيد (إذا تم تمكينها)
* لم يتم تجاوز حد الصفقات اليومية
العناصر البصرية
* خط مرشح النطاق مع تلوين الاتجاه
* الأشرطة العليا والسفلى المستهدفة
* علامات إشارات الدخول
* صناديق نسبة المخاطرة/العائد
* لوحة إعدادات حية
خيارات التخصيص
**التكيف مع السوق:**
* تعديل فترة العينة لبيانات الزمن المختلفة
* تعديل معامل النطاق لمستويات التقلب المختلفة
* تكوين الانتشار/الانزلاق لوسطاء مختلفين
* تحديد النسب المناسبة للمخاطرة/العائد حسب أسلوب التداول
**ضوابط الفلترة:**
* تمكين/تعطيل فلترة الحجم
* تعديل متطلبات التأكيد
* تعيين حدود الصفقات اليومية
* تخصيص تفضيلات التنبيه
الميزات المتعلقة بالأداء
* نتائج اختبار واقعية متوافقة مع التداول المباشر
* القضاء على تحيز المستقبل
* محاكاة تنفيذ الأوامر بشكل صحيح
* إحصائيات تداول شاملة
تكوين التنبيه
**أنواع التنبيهات المتاحة:**
* إشارات الدخول مع معلومات التداول الكاملة
* تنبيهات الإعداد للتحضير المبكر
* إشعارات الخروج لإدارة المراكز
* فلترة التغيرات في الاتجاه لظروف السوق
**تنسيق الرسائل:**
رمز - الإجراء | السعر: XX.XX | الوقف: XX.XX | الهدف: XX.XX | الوقت: HH\:MM
التوصيات لاستخدام الاستراتيجية
**الإعدادات المثلى:**
* بيتكوين/العملات الرقمية الرئيسية: المعلمات الافتراضية
* الفوركس: تقليل فترة العينة إلى 50-70، المعامل إلى 2.0-2.5
* الأسهم: تقليل فترة العينة إلى 30-50، المعامل إلى 1.0-1.8
* الذهب: فترة العينة 60-80، المعامل 1.5-2.0
**تكوين TradingView:**
* إعادة الحساب: "على كل نقطة"
* الأوامر: "استخدام مكبر الشمعة"
* البيانات: يوصى باستخدام التغذية الحية
إخلاء المسؤولية
تم تصميم هذه الاستراتيجية لأغراض تعليمية وتحليلية. الأداء السابق لا يضمن النتائج المستقبلية. يجب دائمًا إجراء اختبارات شاملة على التداول الورقي قبل التنفيذ المباشر. يجب أخذ ظروف السوق، تنفيذ الوسيط، والتحمل الشخصي للمخاطر في الاعتبار عند استخدام أي نظام تداول آلي.
Range Filter Strategy - Real Backtesting
# Overview
Advanced Range Filter strategy designed for realistic backtesting with precise execution timing and comprehensive risk management. Built specifically for cryptocurrency markets with customizable parameters for different assets and timeframes.
Core Algorithm
Range Filter Technology:
- Smooth Average Range calculation using dual EMA filtering
- Dynamic range-based price filtering to identify trend direction
- Anti-noise filtering system to reduce false signals
- Directional momentum tracking with upward/downward counters
Key Features
Real-Time Execution (No Delay)
- Process orders on tick: Immediate execution without waiting for bar close
- Bar magnifier integration for intrabar precision
- Calculate on every tick for maximum responsiveness
- Standard OHLC bypass for enhanced accuracy
Realistic Price Simulation
- HL2 entry pricing (High+Low)/2 for realistic fills
- Configurable spread buffer simulation
- Random slippage generation (0 to max slippage)
- Market liquidity validation before entry
Advanced Signal Filtering
- Volume-based filtering with customizable ratio
- Optional signal confirmation system (1-3 bars)
- Anti-repetition logic to prevent duplicate signals
- Daily trade limit controls
Risk Management
- Fixed Risk:Reward ratios with precise point calculation
- Automatic stop loss and take profit execution
- Position size management
- Maximum daily trades limitation
Alert System
- Real-time alerts synchronized with strategy execution
- Multiple alert types: Setup, Entry, Exit, Status
- Customizable message formatting with price/time inclusion
- TradingView alert panel integration
Default Parameters
Optimized for BTC 5-minute charts:
- Sampling Period: 100
- Range Multiplier: 3.0
- Risk: 50 points
- Reward: 100 points (1:2 R:R)
- Spread Buffer: 2.0 points
- Max Slippage: 1.0 points
Signal Logic
Long Entry Conditions:
- Price above Range Filter line
- Upward momentum confirmed
- Volume requirements met (if enabled)
- Confirmation period completed (if enabled)
- Daily trade limit not exceeded
Short Entry Conditions:
- Price below Range Filter line
- Downward momentum confirmed
- Volume requirements met (if enabled)
- Confirmation period completed (if enabled)
- Daily trade limit not exceeded
Visual Elements
- Range Filter line with directional coloring
- Upper and lower target bands
- Entry signal markers
- Risk/Reward ratio boxes
- Real-time settings dashboard
Customization Options
Market Adaptation:
- Adjust Sampling Period for different timeframes
- Modify Range Multiplier for various volatility levels
- Configure spread/slippage for different brokers
- Set appropriate R:R ratios for trading style
Filtering Controls:
- Enable/disable volume filtering
- Adjust confirmation requirements
- Set daily trade limits
- Customize alert preferences
Performance Features
- Realistic backtesting results aligned with live trading
- Elimination of look-ahead bias
- Proper order execution simulation
- Comprehensive trade statistics
Alert Configuration
Alert Types Available:
- Entry signals with complete trade information
- Setup alerts for early preparation
- Exit notifications for position management
- Filter direction changes for market context
Message Format:
Symbol - Action | Price: XX.XX | Stop: XX.XX | Target: XX.XX | Time: HH:MM
Usage Recommendations
Optimal Settings:
- Bitcoin/Major Crypto: Default parameters
- Forex: Reduce sampling period to 50-70, multiplier to 2.0-2.5
- Stocks: Reduce sampling period to 30-50, multiplier to 1.0-1.8
- Gold: Sampling period 60-80, multiplier 1.5-2.0
TradingView Configuration:
- Recalculate: "On every tick"
- Orders: "Use bar magnifier"
- Data: Real-time feed recommended
Risk Disclaimer
This strategy is designed for educational and analytical purposes. Past performance does not guarantee future results. Always test thoroughly on paper trading before live implementation. Consider market conditions, broker execution, and personal risk tolerance when using any automated trading system.
MACD Liquidity Tracker Strategy [Quant Trading]MACD Liquidity Tracker Strategy
Overview
The MACD Liquidity Tracker Strategy is an enhanced trading system that transforms the traditional MACD indicator into a comprehensive momentum-based strategy with advanced visual signals and risk management. This strategy builds upon the original MACD Liquidity Tracker System indicator by TheNeWSystemLqtyTrckr , converting it into a fully automated trading strategy with improved parameters and additional features.
What Makes This Strategy Original
This strategy significantly enhances the basic MACD approach by introducing:
Four distinct system types for different market conditions and trading styles
Advanced color-coded histogram visualization with four dynamic colors showing momentum strength and direction
Integrated trend filtering using 9 different moving average types
Comprehensive risk management with customizable stop-loss and take-profit levels
Multiple alert systems for entry signals, exits, and trend conditions
Flexible signal display options with customizable entry markers
How It Works
Core MACD Calculation
The strategy uses a fully customizable MACD configuration with traditional default parameters:
Fast MA : 12 periods (customizable, minimum 1, no maximum limit)
Slow MA : 26 periods (customizable, minimum 1, no maximum limit)
Signal Line : 9 periods (customizable, now properly implemented and used)
Cryptocurrency Optimization : The strategy's flexible parameter system allows for significant optimization across different crypto assets. Traditional MACD settings (12/26/9) often generate excessive noise and false signals in volatile crypto markets. By using slower, more smoothed parameters, traders can capture meaningful momentum shifts while filtering out market noise.
Example - DOGE Optimization (45/80/290 settings) :
• Performance : Optimized parameters yielding exceptional backtesting results with 29,800% PnL
• Why it works : DOGE's high volatility and social sentiment-driven price action benefits from heavily smoothed indicators
• Timeframes : Particularly effective on 30-minute and 4-hour charts for swing trading
• Logic : The very slow parameters filter out noise and capture only the most significant trend changes
Other Optimizable Cryptocurrencies : This parameter flexibility makes the strategy highly effective for major altcoins including SUI, SEI, LINK, Solana (SOL) , and many others. Each crypto asset can benefit from custom parameter tuning based on its unique volatility profile and trading characteristics.
Four Trading System Types
1. Normal System (Default)
Long signals : When MACD line is above the signal line
Short signals : When MACD line is below the signal line
Best for : Swing trading and capturing longer-term trends in stable markets
Logic : Traditional MACD crossover approach using the signal line
2. Fast System
Long signals : Bright Blue OR Dark Magenta (transparent) histogram colors
Short signals : Dark Blue (transparent) OR Bright Magenta histogram colors
Best for : Scalping and high-volatility markets (crypto, forex)
Logic : Leverages early momentum shifts based on histogram color changes
3. Safe System
Long signals : Only Bright Blue histogram color (strongest bullish momentum)
Short signals : All other colors (Dark Blue, Bright Magenta, Dark Magenta)
Best for : Risk-averse traders and choppy markets
Logic : Prioritizes only the strongest bullish signals while treating everything else as bearish
4. Crossover System
Long signals : MACD line crosses above signal line
Short signals : MACD line crosses below signal line
Best for : Precise timing entries with traditional MACD methodology
Logic : Pure crossover signals for more precise entry timing
Color-Coded Histogram Logic
The strategy uses four distinct colors to visualize momentum:
🔹 Bright Blue : MACD > 0 and rising (strong bullish momentum)
🔹 Dark Blue (Transparent) : MACD > 0 but falling (weakening bullish momentum)
🔹 Bright Magenta : MACD < 0 and falling (strong bearish momentum)
🔹 Dark Magenta (Transparent) : MACD < 0 but rising (weakening bearish momentum)
Trend Filter Integration
The strategy includes an advanced trend filter using 9 different moving average types:
SMA (Simple Moving Average)
EMA (Exponential Moving Average) - Default
WMA (Weighted Moving Average)
HMA (Hull Moving Average)
RMA (Running Moving Average)
LSMA (Least Squares Moving Average)
DEMA (Double Exponential Moving Average)
TEMA (Triple Exponential Moving Average)
VIDYA (Variable Index Dynamic Average)
Default Settings : 50-period EMA for trend identification
Visual Signal System
Entry Markers : Blue triangles (▲) below candles for long entries, Magenta triangles (▼) above candles for short entries
Candle Coloring : Price candles change color based on active signals (Blue = Long, Magenta = Short)
Signal Text : Optional "Long" or "Short" text inside entry triangles (toggleable)
Trend MA : Gray line plotted on main chart for trend reference
Parameter Optimization Examples
DOGE Trading Success (Optimized Parameters) :
Using 45/80/290 MACD settings with 50-period EMA trend filter has shown exceptional results on DOGE:
Performance : Backtesting results showing 29,800% PnL demonstrate the power of proper parameter optimization
Reasoning : DOGE's meme-driven volatility and social sentiment spikes create significant noise with traditional MACD settings
Solution : Very slow parameters (45/80/290) filter out social media-driven price spikes while capturing only major momentum shifts
Optimal Timeframes : 30-minute and 4-hour charts for swing trading opportunities
Result : Exceptionally clean signals with minimal false entries during DOGE's characteristic pump-and-dump cycles
Multi-Crypto Adaptability :
The same optimization principles apply to other major cryptocurrencies:
SUI : Benefits from smoothed parameters due to newer coin volatility patterns
SEI : Requires adjustment for its unique DeFi-related price movements
LINK : Oracle news events create price spikes that benefit from noise filtering
Solana (SOL) : Network congestion events and ecosystem developments need smoothed detection
General Rule : Higher volatility coins typically benefit from very slow MACD parameters (40-50 / 70-90 / 250-300 ranges)
Key Input Parameters
System Type : Choose between Fast, Normal, Safe, or Crossover (Default: Normal)
MACD Fast MA : 12 periods default (no maximum limit, consider 40-50 for crypto optimization)
MACD Slow MA : 26 periods default (no maximum limit, consider 70-90 for crypto optimization)
MACD Signal MA : 9 periods default (now properly utilized, consider 250-300 for crypto optimization)
Trend MA Type : EMA default (9 options available)
Trend MA Length : 50 periods default (no maximum limit)
Signal Display : Both, Long Only, Short Only, or None
Show Signal Text : True/False toggle for entry marker text
Trading Applications
Recommended Use Cases
Momentum Trading : Capitalize on strong directional moves using the color-coded system
Trend Following : Combine MACD signals with trend MA filter for higher probability trades
Scalping : Use "Fast" system type for quick entries in volatile markets
Swing Trading : Use "Normal" or "Safe" system types for longer-term positions
Cryptocurrency Trading : Optimize parameters for individual crypto assets (e.g., 45/80/290 for DOGE, custom settings for SUI, SEI, LINK, SOL)
Market Suitability
Volatile Markets : Forex, crypto, indices (recommend "Fast" system or smoothed parameters)
Stable Markets : Stocks, ETFs (recommend "Normal" or "Safe" system)
All Timeframes : Effective from 1-minute charts to daily charts
Crypto Optimization : Each major cryptocurrency (DOGE, SUI, SEI, LINK, SOL, etc.) can benefit from custom parameter tuning. Consider slower MACD parameters for noise reduction in volatile crypto markets
Alert System
The strategy provides comprehensive alerts for:
Entry Signals : Long and short entry triangle appearances
Exit Signals : Position exit notifications
Color Changes : Individual histogram color alerts
Trend Conditions : Price above/below trend MA alerts
Strategy Parameters
Default Settings
Initial Capital : $1,000
Position Size : 100% of equity
Commission : 0.1%
Slippage : 3 points
Date Range : January 1, 2018 to December 31, 2069
Risk Management (Optional)
Stop Loss : Disabled by default (customizable percentage-based)
Take Profit : Disabled by default (customizable percentage-based)
Short Trades : Disabled by default (can be enabled)
Important Notes and Limitations
Backtesting Considerations
Uses realistic commission (0.1%) and slippage (3 points)
Default position sizing uses 100% equity - adjust based on risk tolerance
Stop-loss and take-profit are disabled by default to show raw strategy performance
Strategy does not use lookahead bias or future data
Risk Warnings
Past performance does not guarantee future results
MACD-based strategies may produce false signals in ranging markets
Consider combining with additional confluences like support/resistance levels
Test thoroughly on demo accounts before live trading
Adjust position sizing based on your risk management requirements
Technical Limitations
Strategy does not work on non-standard chart types (Heikin Ashi, Renko, etc.)
Signals are based on close prices and may not reflect intraday price action
Multiple rapid signals in volatile conditions may result in overtrading
Credits and Attribution
This strategy is based on the original "MACD Liquidity Tracker System" indicator created by TheNeWSystemLqtyTrckr . This strategy version includes significant enhancements:
Complete strategy implementation with entry/exit logic
Addition of the "Crossover" system type
Proper implementation and utilization of the MACD signal line
Enhanced risk management features
Improved parameter flexibility with no artificial maximum limits
Additional alert systems for comprehensive trade management
The original indicator's core color logic and visual system have been preserved while expanding functionality for automated trading applications.
Zero-Lag RSI DivergenceZero-Lag RSI Divergence
Overview
This indicator identifies RSI divergences in real-time without delay, providing immediate signals as price-momentum discrepancies develop. The indicator analyzes price action against RSI momentum across dual configurable periods, enabling traders to detect potential reversal opportunities with zero lag.
Key Features
Instant Divergence Detection : Identifies bullish and bearish divergences immediately upon formation without waiting for candle confirmation or historical validation. This eliminates signal delay but may increase false signals due to higher sensitivity.
Dual Period Analysis : Configure detection across two independent cycles - Short Period (default 15) and Long Period (default 50) - allowing for multi-timeframe divergence analysis and enhanced signal validation across different market conditions.
Visual Divergence Lines : Automatically draws dashed lines connecting divergence points between price highs/lows and corresponding RSI peaks/troughs, clearly illustrating the momentum-price relationship.
Customizable RSI Parameters : Adjustable RSI length (default 14) allows optimization for different market volatility and trading timeframes.
How It Works
The indicator continuously monitors price action patterns and RSI momentum:
- Bullish Divergence : Detected when price makes lower lows while RSI makes higher lows, suggesting potential upward momentum
- Bearish Divergence : Identified when price makes higher highs while RSI makes lower highs, indicating potential downward momentum
The algorithm uses candle color transitions and immediate RSI comparisons to trigger signals without historical repainting , ensuring backtesting accuracy and real-time reliability.
How To Read
Important Notes
Higher Signal Frequency : The zero-lag approach increases signal sensitivity, generating more frequent alerts that may include false signals. Consider using additional confirmation methods for trade entries.
Non-Repainting : All signals are generated and maintained without historical modification, ensuring consistent backtesting and forward-testing results.
Input Parameters
RSI Length: Period for RSI calculation (default: 14)
Short/Long Periods: Lookback periods for divergence detection (default: 15/50)
Line Colors: Customizable colors for short and long period divergence lines
Label Settings: Optional divergence labels with custom text
This indicator is designed for traders seeking immediate divergence identification across multiple timeframes while maintaining signal integrity and backtesting reliability.
VoVix DEVMA🌌 VoVix DEVMA: A Deep Dive into Second-Order Volatility Dynamics
Welcome to VoVix+, a sophisticated trading framework that transcends traditional price analysis. This is not merely another indicator; it is a complete system designed to dissect and interpret the very fabric of market volatility. VoVix+ operates on the principle that the most powerful signals are not found in price alone, but in the behavior of volatility itself. It analyzes the rate of change, the momentum, and the structure of market volatility to identify periods of expansion and contraction, providing a unique edge in anticipating major market moves.
This document will serve as your comprehensive guide, breaking down every mathematical component, every user input, and every visual element to empower you with a profound understanding of how to harness its capabilities.
🔬 THEORETICAL FOUNDATION: THE MATHEMATICS OF MARKET DYNAMICS
VoVix+ is built upon a multi-layered mathematical engine designed to measure what we call "second-order volatility." While standard indicators analyze price, and first-order volatility indicators (like ATR) analyze the range of price, VoVix+ analyzes the dynamics of the volatility itself. This provides insight into the market's underlying state of stability or chaos.
1. The VoVix Score: Measuring Volatility Thrust
The core of the system begins with the VoVix Score. This is a normalized measure of volatility acceleration or deceleration.
Mathematical Formula:
VoVix Score = (ATR(fast) - ATR(slow)) / (StDev(ATR(fast)) + ε)
Where:
ATR(fast) is the Average True Range over a short period, representing current, immediate volatility.
ATR(slow) is the Average True Range over a longer period, representing the baseline or established volatility.
StDev(ATR(fast)) is the Standard Deviation of the fast ATR, which measures the "noisiness" or consistency of recent volatility.
ε (epsilon) is a very small number to prevent division by zero.
Market Implementation:
Positive Score (Expansion): When the fast ATR is significantly higher than the slow ATR, it indicates a rapid increase in volatility. The market is "stretching" or expanding.
Negative Score (Contraction): When the fast ATR falls below the slow ATR, it indicates a decrease in volatility. The market is "coiling" or contracting.
Normalization: By dividing by the standard deviation, we normalize the score. This turns it into a standardized measure, allowing us to compare volatility thrust across different market conditions and timeframes. A score of 2.0 in a quiet market means the same, relatively, as a score of 2.0 in a volatile market.
2. Deviation Analysis (DEV): Gauging Volatility's Own Volatility
The script then takes the analysis a step further. It calculates the standard deviation of the VoVix Score itself.
Mathematical Formula:
DEV = StDev(VoVix Score, lookback_period)
Market Implementation:
This DEV value represents the magnitude of chaos or stability in the market's volatility dynamics. A high DEV value means the volatility thrust is erratic and unpredictable. A low DEV value suggests the change in volatility is smooth and directional.
3. The DEVMA Crossover: Identifying Regime Shifts
This is the primary signal generator. We take two moving averages of the DEV value.
Mathematical Formula:
fastDEVMA = SMA(DEV, fast_period)
slowDEVMA = SMA(DEV, slow_period)
The Core Signal:
The strategy triggers on the crossover and crossunder of these two DEVMA lines. This is a profound concept: we are not looking at a moving average of price or even of volatility, but a moving average of the standard deviation of the normalized rate of change of volatility.
Bullish Crossover (fastDEVMA > slowDEVMA): This signals that the short-term measure of volatility's chaos is increasing relative to the long-term measure. This often precedes a significant market expansion and is interpreted as a bullish volatility regime.
Bearish Crossunder (fastDEVMA < slowDEVMA): This signals that the short-term measure of volatility's chaos is decreasing. The market is settling down or contracting, often leading to trending moves or range consolidation.
⚙️ INPUTS MENU: CONFIGURING YOUR ANALYSIS ENGINE
Every input has been meticulously designed to give you full control over the strategy's behavior. Understanding these settings is key to adapting VoVix+ to your specific instrument, timeframe, and trading style.
🌀 VoVix DEVMA Configuration
🧬 Deviation Lookback: This sets the lookback period for calculating the DEV value. It defines the window for measuring the stability of the VoVix Score. A shorter value makes the system highly reactive to recent changes in volatility's character, ideal for scalping. A longer value provides a smoother, more stable reading, better for identifying major, long-term regime shifts.
⚡ Fast VoVix Length: This is the lookback period for the fastDEVMA. It represents the short-term trend of volatility's chaos. A smaller number will result in a faster, more sensitive signal line that reacts quickly to market shifts.
🐌 Slow VoVix Length: This is the lookback period for the slowDEVMA. It represents the long-term, baseline trend of volatility's chaos. A larger number creates a more stable, slower-moving anchor against which the fast line is compared.
How to Optimize: The relationship between the Fast and Slow lengths is crucial. A wider gap (e.g., 20 and 60) will result in fewer, but potentially more significant, signals. A narrower gap (e.g., 25 and 40) will generate more frequent signals, suitable for more active trading styles.
🧠 Adaptive Intelligence
🧠 Enable Adaptive Features: When enabled, this activates the strategy's performance tracking module. The script will analyze the outcome of its last 50 trades to calculate a dynamic win rate.
⏰ Adaptive Time-Based Exit: If Enable Adaptive Features is on, this allows the strategy to adjust its Maximum Bars in Trade setting based on performance. It learns from the average duration of winning trades. If winning trades tend to be short, it may shorten the time exit to lock in profits. If winners tend to run, it will extend the time exit, allowing trades more room to develop. This helps prevent the strategy from cutting winning trades short or holding losing trades for too long.
⚡ Intelligent Execution
📊 Trade Quantity: A straightforward input that defines the number of contracts or shares for each trade. This is a fixed value for consistent position sizing.
🛡️ Smart Stop Loss: Enables the dynamic stop-loss mechanism.
🎯 Stop Loss ATR Multiplier: Determines the distance of the stop loss from the entry price, calculated as a multiple of the current 14-period ATR. A higher multiplier gives the trade more room to breathe but increases risk per trade. A lower multiplier creates a tighter stop, reducing risk but increasing the chance of being stopped out by normal market noise.
💰 Take Profit ATR Multiplier: Sets the take profit target, also as a multiple of the ATR. A common practice is to set this higher than the Stop Loss multiplier (e.g., a 2:1 or 3:1 reward-to-risk ratio).
🏃 Use Trailing Stop: This is a powerful feature for trend-following. When enabled, instead of a fixed stop loss, the stop will trail behind the price as the trade moves into profit, helping to lock in gains while letting winners run.
🎯 Trail Points & 📏 Trail Offset ATR Multipliers: These control the trailing stop's behavior. Trail Points defines how much profit is needed before the trail activates. Trail Offset defines how far the stop will trail behind the current price. Both are based on ATR, making them fully adaptive to market volatility.
⏰ Maximum Bars in Trade: This is a time-based stop. It forces an exit if a trade has been open for a specified number of bars, preventing positions from being held indefinitely in stagnant markets.
⏰ Session Management
These inputs allow you to confine the strategy's trading activity to specific market hours, which is crucial for day trading instruments that have defined high-volume sessions (e.g., stock market open).
🎨 Visual Effects & Dashboard
These toggles give you complete control over the on-chart visuals and the dashboard. You can disable any element to declutter your chart or focus only on the information that matters most to you.
📊 THE DASHBOARD: YOUR AT-A-GLANCE COMMAND CENTER
The dashboard centralizes all critical information into one compact, easy-to-read panel. It provides a real-time summary of the market state and strategy performance.
🎯 VOVIX ANALYSIS
Fast & Slow: Displays the current numerical values of the fastDEVMA and slowDEVMA. The color indicates their direction: green for rising, red for falling. This lets you see the underlying momentum of each line.
Regime: This is your most important environmental cue. It tells you the market's current state based on the DEVMA relationship. 🚀 EXPANSION (Green) signifies a bullish volatility regime where explosive moves are more likely. ⚛️ CONTRACTION (Purple) signifies a bearish volatility regime, where the market may be consolidating or entering a smoother trend.
Quality: Measures the strength of the last signal based on the magnitude of the DEVMA difference. An ELITE or STRONG signal indicates a high-conviction setup where the crossover had significant force.
PERFORMANCE
Win Rate & Trades: Displays the historical win rate of the strategy from the backtest, along with the total number of closed trades. This provides immediate feedback on the strategy's historical effectiveness on the current chart.
EXECUTION
Trade Qty: Shows your configured position size per trade.
Session: Indicates whether trading is currently OPEN (allowed) or CLOSED based on your session management settings.
POSITION
Position & PnL: Displays your current position (LONG, SHORT, or FLAT) and the real-time Profit or Loss of the open trade.
🧠 ADAPTIVE STATUS
Stop/Profit Mult: In this simplified version, these are placeholders. The primary adaptive feature currently modifies the time-based exit, which is reflected in how long trades are held on the chart.
🎨 THE VISUAL UNIVERSE: DECIPHERING MARKET GEOMETRY
The visuals are not mere decorations; they are geometric representations of the underlying mathematical concepts, designed to give you an intuitive feel for the market's state.
The Core Lines:
FastDEVMA (Green/Maroon Line): The primary signal line. Green when rising, indicating an increase in short-term volatility chaos. Maroon when falling.
SlowDEVMA (Aqua/Orange Line): The baseline. Aqua when rising, indicating a long-term increase in volatility chaos. Orange when falling.
🌊 Morphism Flow (Flowing Lines with Circles):
What it represents: This visualizes the momentum and strength of the fastDEVMA. The width and intensity of the "beam" are proportional to the signal strength.
Interpretation: A thick, steep, and vibrant flow indicates powerful, committed momentum in the current volatility regime. The floating '●' particles represent kinetic energy; more particles suggest stronger underlying force.
📐 Homotopy Paths (Layered Transparent Boxes):
What it represents: These layered boxes are centered between the two DEVMA lines. Their height is determined by the DEV value.
Interpretation: This visualizes the overall "volatility of volatility." Wider boxes indicate a chaotic, unpredictable market. Narrower boxes suggest a more stable, predictable environment.
🧠 Consciousness Field (The Grid):
What it represents: This grid provides a historical lookback at the DEV range.
Interpretation: It maps the recent "consciousness" or character of the market's volatility. A consistently wide grid suggests a prolonged period of chaos, while a narrowing grid can signal a transition to a more stable state.
📏 Functorial Levels (Projected Horizontal Lines):
What it represents: These lines extend from the current fastDEVMA and slowDEVMA values into the future.
Interpretation: Think of these as dynamic support and resistance levels for the volatility structure itself. A crossover becomes more significant if it breaks cleanly through a prior established level.
🌊 Flow Boxes (Spaced Out Boxes):
What it represents: These are compact visual footprints of the current regime, colored green for Expansion and red for Contraction.
Interpretation: They provide a quick, at-a-glance confirmation of the dominant volatility flow, reinforcing the background color.
Background Color:
This provides an immediate, unmistakable indication of the current volatility regime. Light Green for Expansion and Light Aqua/Blue for Contraction, allowing you to assess the market environment in a split second.
📊 BACKTESTING PERFORMANCE REVIEW & ANALYSIS
The following is a factual, transparent review of a backtest conducted using the strategy's default settings on a specific instrument and timeframe. This information is presented for educational purposes to demonstrate how the strategy's mechanics performed over a historical period. It is crucial to understand that these results are historical, apply only to the specific conditions of this test, and are not a guarantee or promise of future performance. Market conditions are dynamic and constantly change.
Test Parameters & Conditions
To ensure the backtest reflects a degree of real-world conditions, the following parameters were used. The goal is to provide a transparent baseline, not an over-optimized or unrealistic scenario.
Instrument: CME E-mini Nasdaq 100 Futures (NQ1!)
Timeframe: 5-Minute Chart
Backtesting Range: March 24, 2024, to July 09, 2024
Initial Capital: $100,000
Commission: $0.62 per contract (A realistic cost for futures trading).
Slippage: 3 ticks per trade (A conservative setting to account for potential price discrepancies between order placement and execution).
Trade Size: 1 contract per trade.
Performance Overview (Historical Data)
The test period generated 465 total trades , providing a statistically significant sample size for analysis, which is well above the recommended minimum of 100 trades for a strategy evaluation.
Profit Factor: The historical Profit Factor was 2.663 . This metric represents the gross profit divided by the gross loss. In this test, it indicates that for every dollar lost, $2.663 was gained.
Percent Profitable: Across all 465 trades, the strategy had a historical win rate of 84.09% . While a high figure, this is a historical artifact of this specific data set and settings, and should not be the sole basis for future expectations.
Risk & Trade Characteristics
Beyond the headline numbers, the following metrics provide deeper insight into the strategy's historical behavior.
Sortino Ratio (Downside Risk): The Sortino Ratio was 6.828 . Unlike the Sharpe Ratio, this metric only measures the volatility of negative returns. A higher value, such as this one, suggests that during this test period, the strategy was highly efficient at managing downside volatility and large losing trades relative to the profits it generated.
Average Trade Duration: A critical characteristic to understand is the strategy's holding period. With an average of only 2 bars per trade , this configuration operates as a very short-term, or scalping-style, system. Winning trades averaged 2 bars, while losing trades averaged 4 bars. This indicates the strategy's logic is designed to capture quick, high-probability moves and exit rapidly, either at a profit target or a stop loss.
Conclusion and Final Disclaimer
This backtest demonstrates one specific application of the VoVix+ framework. It highlights the strategy's behavior as a short-term system that, in this historical test on NQ1!, exhibited a high win rate and effective management of downside risk. Users are strongly encouraged to conduct their own backtests on different instruments, timeframes, and date ranges to understand how the strategy adapts to varying market structures. Past performance is not indicative of future results, and all trading involves significant risk.
🔧 THE DEVELOPMENT PHILOSOPHY: FROM VOLATILITY TO CLARITY
The journey to create VoVix+ began with a simple question: "What drives major market moves?" The answer is often not a change in price direction, but a fundamental shift in market volatility. Standard indicators are reactive to price. We wanted to create a system that was predictive of market state. VoVix+ was designed to go one level deeper—to analyze the behavior, character, and momentum of volatility itself.
The challenge was twofold. First, to create a robust mathematical model to quantify these abstract concepts. This led to the multi-layered analysis of ATR differentials and standard deviations. Second, to make this complex data intuitive and actionable. This drove the creation of the "Visual Universe," where abstract mathematical values are translated into geometric shapes, flows, and fields. The adaptive system was intentionally kept simple and transparent, focusing on a single, impactful parameter (time-based exits) to provide performance feedback without becoming an inscrutable "black box." The result is a tool that is both profoundly deep in its analysis and remarkably clear in its presentation.
⚠️ RISK DISCLAIMER AND BEST PRACTICES
VoVix+ is an advanced analytical tool, not a guarantee of future profits. All financial markets carry inherent risk. The backtesting results shown by the strategy are historical and do not guarantee future performance. This strategy incorporates realistic commission and slippage settings by default, but market conditions can vary. Always practice sound risk management, use position sizes appropriate for your account equity, and never risk more than you can afford to lose. It is recommended to use this strategy as part of a comprehensive trading plan. This was developed specifically for Futures
"The prevailing wisdom is that markets are always right. I take the opposite view. I assume that markets are always wrong. Even if my assumption is occasionally wrong, I use it as a working hypothesis."
— George Soros
— Dskyz, Trade with insight. Trade with anticipation.
Multi-Confluence Swing Hunter V1# Multi-Confluence Swing Hunter V1 - Complete Description
Overview
The Multi-Confluence Swing Hunter V1 is a sophisticated low timeframe scalping strategy specifically optimized for MSTR (MicroStrategy) trading. This strategy employs a comprehensive point-based scoring system that combines optimized technical indicators, price action analysis, and reversal pattern recognition to generate precise trading signals on lower timeframes.
Performance Highlight:
In backtesting on MSTR 5-minute charts, this strategy has demonstrated over 200% profit performance, showcasing its effectiveness in capturing rapid price movements and volatility patterns unique to MicroStrategy's trading behavior.
The strategy's parameters have been fine-tuned for MSTR's unique volatility characteristics, though they can be optimized for other high-volatility instruments as well.
## Key Innovation & Originality
This strategy introduces a unique **dual scoring system** approach:
- **Entry Scoring**: Identifies swing bottoms using 13+ different technical criteria
- **Exit Scoring**: Identifies swing tops using inverse criteria for optimal exit timing
Unlike traditional strategies that rely on simple indicator crossovers, this system quantifies market conditions through a weighted scoring mechanism, providing objective, data-driven entry and exit decisions.
## Technical Foundation
### Optimized Indicator Parameters
The strategy utilizes extensively backtested parameters specifically optimized for MSTR's volatility patterns:
**MACD Configuration (3,10,3)**:
- Fast EMA: 3 periods (vs standard 12)
- Slow EMA: 10 periods (vs standard 26)
- Signal Line: 3 periods (vs standard 9)
- **Rationale**: These faster parameters provide earlier signal detection while maintaining reliability, particularly effective for MSTR's rapid price movements and high-frequency volatility
**RSI Configuration (21-period)**:
- Length: 21 periods (vs standard 14)
- Oversold: 30 level
- Extreme Oversold: 25 level
- **Rationale**: The 21-period RSI reduces false signals while still capturing oversold conditions effectively in MSTR's volatile environment
**Parameter Adaptability**: While optimized for MSTR, these parameters can be adjusted for other high-volatility instruments. Faster-moving stocks may benefit from even shorter MACD periods, while less volatile assets might require longer periods for optimal performance.
### Scoring System Methodology
**Entry Score Components (Minimum 13 points required)**:
1. **RSI Signals** (max 5 points):
- RSI < 30: +2 points
- RSI < 25: +2 points
- RSI turning up: +1 point
2. **MACD Signals** (max 8 points):
- MACD below zero: +1 point
- MACD turning up: +2 points
- MACD histogram improving: +2 points
- MACD bullish divergence: +3 points
3. **Price Action** (max 4 points):
- Long lower wick (>50%): +2 points
- Small body (<30%): +1 point
- Bullish close: +1 point
4. **Pattern Recognition** (max 8 points):
- RSI bullish divergence: +4 points
- Quick recovery pattern: +2 points
- Reversal confirmation: +4 points
**Exit Score Components (Minimum 13 points required)**:
Uses inverse criteria to identify swing tops with similar weighting system.
## Risk Management Features
### Position Sizing & Risk Control
- **Single Position Strategy**: 100% equity allocation per trade
- **No Overlapping Positions**: Ensures focused risk management
- **Configurable Risk/Reward**: Default 5:1 ratio optimized for volatile assets
### Stop Loss & Take Profit Logic
- **Dynamic Stop Loss**: Based on recent swing lows with configurable buffer
- **Risk-Based Take Profit**: Calculated using risk/reward ratio
- **Clean Exit Logic**: Prevents conflicting signals
## Default Settings Optimization
### Key Parameters (Optimized for MSTR/Bitcoin-style volatility):
- **Minimum Entry Score**: 13 (ensures high-conviction entries)
- **Minimum Exit Score**: 13 (prevents premature exits)
- **Risk/Reward Ratio**: 5.0 (accounts for volatility)
- **Lower Wick Threshold**: 50% (identifies true hammer patterns)
- **Divergence Lookback**: 8 bars (optimal for swing timeframes)
### Why These Defaults Work for MSTR:
1. **Higher Score Thresholds**: MSTR's volatility requires more confirmation
2. **5:1 Risk/Reward**: Compensates for wider stops needed in volatile markets
3. **Faster MACD**: Captures momentum shifts quickly in fast-moving stocks
4. **21-period RSI**: Reduces noise while maintaining sensitivity
## Visual Features
### Score Display System
- **Green Labels**: Entry scores ≥10 points (below bars)
- **Red Labels**: Exit scores ≥10 points (above bars)
- **Large Triangles**: Actual trade entries/exits
- **Small Triangles**: Reversal pattern confirmations
### Chart Cleanliness
- Indicators plotted in separate panes (MACD, RSI)
- TP/SL levels shown only during active positions
- Clear trade markers distinguish signals from actual trades
## Backtesting Specifications
### Realistic Trading Conditions
- **Commission**: 0.1% per trade
- **Slippage**: 3 points
- **Initial Capital**: $1,000
- **Account Type**: Cash (no margin)
### Sample Size Considerations
- Strategy designed for 100+ trade sample sizes
- Recommended timeframes: 4H, 1D for swing trading
- Optimal for trending/volatile markets
## Strategy Limitations & Considerations
### Market Conditions
- **Best Performance**: Trending markets with clear swings
- **Reduced Effectiveness**: Highly choppy, sideways markets
- **Volatility Dependency**: Optimized for moderate to high volatility assets
### Risk Warnings
- **High Allocation**: 100% position sizing increases risk
- **No Diversification**: Single position strategy
- **Backtesting Limitation**: Past performance doesn't guarantee future results
## Usage Guidelines
### Recommended Assets & Timeframes
- **Primary Target**: MSTR (MicroStrategy) - 5min to 15min timeframes
- **Secondary Targets**: High-volatility stocks (TSLA, NVDA, COIN, etc.)
- **Crypto Markets**: Bitcoin, Ethereum (with parameter adjustments)
- **Timeframe Optimization**: 1min-15min for scalping, 30min-1H for swing scalping
### Timeframe Recommendations
- **Primary Scalping**: 5-minute and 15-minute charts
- **Active Monitoring**: 1-minute for precise entries
- **Swing Scalping**: 30-minute to 1-hour timeframes
- **Avoid**: Sub-1-minute (excessive noise) and above 4-hour (reduces scalping opportunities)
## Technical Requirements
- **Pine Script Version**: v6
- **Overlay**: Yes (plots on price chart)
- **Additional Panes**: MACD and RSI indicators
- **Real-time Compatibility**: Confirmed bar signals only
## Customization Options
All parameters are fully customizable through inputs:
- Indicator lengths and levels
- Scoring thresholds
- Risk management settings
- Visual display preferences
- Date range filtering
## Conclusion
This scalping strategy represents a comprehensive approach to low timeframe trading that combines multiple technical analysis methods into a cohesive, quantified system specifically optimized for MSTR's unique volatility characteristics. The optimized parameters and scoring methodology provide a systematic way to identify high-probability scalping setups while managing risk effectively in fast-moving markets.
The strategy's strength lies in its objective, multi-criteria approach that removes emotional decision-making from scalping while maintaining the flexibility to adapt to different instruments through parameter optimization. While designed for MSTR, the underlying methodology can be fine-tuned for other high-volatility assets across various markets.
**Important Disclaimer**: This strategy is designed for experienced scalpers and is optimized for MSTR trading. The high-frequency nature of scalping involves significant risk. Past performance does not guarantee future results. Always conduct your own analysis, consider your risk tolerance, and be aware of commission/slippage costs that can significantly impact scalping profitability.
Arbitrage Spot-Futures Don++Strategy: Spot-Futures Arbitrage Don++
This strategy has been designed to detect and exploit arbitrage opportunities between the Spot and Futures markets of the same trading pair (e.g. BTC/USDT). The aim is to take advantage of price differences (spreads) between the two markets, while minimizing risk through dynamic position management.
[Operating principle
The strategy is based on calculating the spread between Spot and Futures prices. When this spread exceeds a certain threshold (positive or negative), reverse positions are opened simultaneously on both markets:
- i] Long Spot + Short Futures when the spread is positive.
- i] Short Spot + Long Futures when the spread is negative.
Positions are closed when the spread returns to a value close to zero or after a user-defined maximum duration.
[Strategy strengths
1. Adaptive thresholds :
- Entry/exit thresholds can be dynamic (based on moving averages and standard deviations) or fixed, offering greater flexibility to adapt to market conditions.
2. Robust data management :
- The script checks the validity of data before executing calculations, thus avoiding errors linked to missing or invalid data.
3. Risk limitation :
- A position size based on a percentage of available capital (default 10%) limits exposure.
- A time filter limits the maximum duration of positions to avoid losses due to persistent spreads.
4. Clear visualization :
- Charts include horizontal lines for entry/exit thresholds, as well as visual indicators for spread and Spot/Futures prices.
5. Alerts and logs :
- Alerts are triggered on entries and exits to inform the user in real time.
[Points for improvement or completion
Although this strategy is functional and robust, it still has a few limitations that could be addressed in future versions:
1. [Limited historical data :
- TradingView does not retrieve real-time data for multiple symbols simultaneously. This can limit the accuracy of calculations, especially under conditions of high volatility.
2. [Lack of liquidity management :
- The script does not take into account the volumes available on the order books. In conditions of low liquidity, it may be difficult to execute orders at the desired prices.
3. [Non-dynamic transaction costs :
- Transaction costs (exchange fees, slippage) are set manually. A dynamic integration of these costs via an external API would be more realistic.
4. User-dependency for symbols :
- Users must manually specify Spot and Futures symbols. Automatic symbol validation would be useful to avoid configuration errors.
5. Lack of advanced backtesting :
- Backtesting is based solely on historical data available on TradingView. An implementation with third-party data (via an API) would enable the strategy to be tested under more realistic conditions.
6. [Parameter optimization :
- Certain parameters (such as analysis period or spread thresholds) could be optimized for each specific trading pair.
[How can I contribute?
If you'd like to help improve this strategy, here are a few ideas:
1. Add additional filters:
- For example, a filter based on volume or volatility to avoid false signals.
2. Integrate dynamic costs:
- Use an external API to retrieve actual costs and adjust thresholds accordingly.
3. Improve position management:
- Implement hedging or scalping mechanisms to maximize profits.
4. Test on other pairs:
- Evaluate the strategy's performance on other assets (ETH, SOL, etc.) and adjust parameters accordingly.
5. Publish backtesting results :
- Share detailed analyses of the strategy's performance under different market conditions.
[Conclusion
This Spot-Futures arbitrage strategy is a powerful tool for exploiting price differentials between markets. Although it is already functional, it can still be improved to meet more complex trading scenarios. Feel free to test, modify and share your ideas to make this strategy even more effective!
[Thank you for contributing to this open-source community!
If you have any questions or suggestions, please feel free to comment or contact me directly.
Reversal & Breakout Strategy with ORB### Reversal & Breakout Strategy with ORB
This strategy combines three distinct trading approaches—reversals, trend breakouts, and opening range breakouts (ORB)—into a single, cohesive system. The goal is to capture high-probability setups across different market conditions, leveraging a mashup of technical indicators for confirmation and risk management. Below, I’ll explain why this combination works, how the components interact, and how to use it effectively.
#### Why the Mashup?
- **Reversals**: Identifies overextended moves using RSI (overbought/oversold) and SMA50 crosses, filtered by VWAP and SMA200 trend direction. This targets mean-reversion opportunities in trending markets.
- **Breakouts**: Uses EMA9/EMA20 crossovers with VWAP and SMA200 confirmation to catch momentum-driven trend continuations.
- **Opening Range Breakout (ORB)**: Detects early momentum by breaking the high/low of a user-defined opening range (default: 15 bars) with volume confirmation. This adds a time-based edge, ideal for intraday trading.
The synergy comes from blending these methods: reversals catch pullbacks, breakouts ride trends, and ORB exploits early volatility—all filtered by trend (SMA200) and anchored by VWAP for context.
#### How It Works
1. **Indicators**:
- **EMA9/EMA20**: Fast-moving averages for breakout signals.
- **SMA50**: Medium-term trend filter for reversals.
- **SMA200**: Long-term trend direction to align trades.
- **RSI (14)**: Measures overbought (>70) or oversold (<30) conditions.
- **VWAP**: Acts as a dynamic support/resistance level.
- **ATR (14)**: Sets stop-loss distance (default: 1.5x ATR).
- **Volume**: Confirms ORB breakouts (1.5x average volume of opening range).
2. **Entry Conditions**:
- **Long**: Triggers on reversal (SMA50 cross + RSI < 30 + below VWAP + uptrend), breakout (EMA9 > EMA20 + above VWAP + uptrend), or ORB (break above opening range high + volume).
- **Short**: Triggers on reversal (SMA50 cross + RSI > 70 + above VWAP + downtrend), breakout (EMA9 < EMA20 + below VWAP + downtrend), or ORB (break below opening range low + volume).
3. **Risk Management**:
- Risks 5% of equity per trade (based on the initial capital set in the strategy tester).
- Stop-loss: Based on lowest low/highest high over 7 bars ± 1.5x ATR.
- Targets: Two exits at 1:1 and 1:2 risk:reward (50% of position at each).
- Break-even: Stop moves to entry price after the first target is hit.
4. **Backtesting Settings**:
- Commission: Hardcoded at 0.1% per trade (realistic for most brokers).
- Slippage: Hardcoded at 2 ticks (realistic for most markets).
- Tested on datasets yielding 100+ trades (e.g., 2-min or 5-min charts over months).
#### How to Use It
- **Timeframe**: Works best on intraday (2-min, 5-min) or daily charts. Adjust `Opening Range Bars` (e.g., 15 bars = 30 min on 2-min chart) for your timeframe.
- **Settings**:
- Set your initial equity in the TradingView strategy tester’s "Properties" tab under "Initial Capital" (e.g., $10,000). The script automatically risks 5% of this equity per trade.
- Adjust `Stop Loss ATR Multiplier` or `Risk:Reward Targets` based on your risk tolerance.
- Note that commission (0.1%) and slippage (2 ticks) are fixed in the script for backtesting consistency.
- **Execution**: Enter on signal, monitor plotted stop (red) and targets (green/blue). The strategy supports pyramiding (up to 2 positions) for scaling into trends.
#### Backtesting Notes
Results are realistic with commission (0.1%) and slippage (2 ticks) included. For a sufficient sample, test on volatile instruments (e.g., stocks, forex) over 3-6 months on lower timeframes. The default 1.5x ATR stop may seem wide, but it’s justified to avoid premature exits in volatile markets—feel free to tweak it with justification. The script assumes an initial capital of $10,000 in the strategy tester for the 5% risk calculation (e.g., $500 risk per trade); adjust this in the "Properties" tab as needed.
This mashup isn’t just a random mix; it’s a deliberate fusion of complementary strategies, offering traders flexibility across market phases. Questions? Let me know!
Naive Bayes Candlestick Pattern Classifier v1.1 BETAAn intermezzo on why i made this script publication..
A : Candlestick Pattern took hours to backtest, why not using Machine Learning techniques?
B : Machine Learning, no that's gonna be really heavy bro!
A : Not really, because we use Naive Bayes.
B : The simplest, yet powerful machine learning algorithm to separate (a.k.a classify) multivariate data.
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Hello, everyone!
After deep research in extracting meaningful information from the market, I ended up building this powerful machine learning indicator based on the evolution of Bayesian Statistics. This indicator not only leverages the simplicity of Naive Bayes but also extends its application to candlestick pattern analysis, making it an invaluable tool for traders who are looking to enhance their technical analysis without spending countless hours manually backtesting each pattern on each market!.
What most interesting part is actually after learning all of likely useless methods like fibonacci, supply and demand, volume profile, etc. We always ended up back to basic like support and resistance and candlestick patterns, but with a slight twist on strategy algorithm design and statistical approach. Thus, the only reason why i made this, because i exactly know that you guys will ended up in this position as time goes by.
The essence of this indicator lies in its ability to automate the recognition and statistical evaluation of various candlestick patterns. Traditionally, traders have relied on visual inspection and manual backtesting to determine the effectiveness of patterns like Bullish Engulfing, Bearish Engulfing, Harami variations, Hammer formations, and even more complex multi-candle patterns such as Three White Soldiers, Three Black Crows, Dark Cloud Cover, and Piercing Pattern. However, these conventional methods are both time-consuming and prone to subjective bias.
To address these challenges, I employed Naive Bayes—a probabilistic classifier that, despite its simplicity, offers robust performance in various domains. Naive Bayes assumes that each feature is independent of the others given the class label, which, although a strong assumption, works remarkably well in practice, especially when the dataset is large like market data and the feature space is high-dimensional. In our case, each candlestick pattern acts as a feature that can be statistically evaluated based on its historical performance. The indicator calculates a probability that a given pattern will lead to a price reversal, by comparing the pattern’s close price to the highest or lowest price achieved in a lookahead window.
One of the standout features of this script is its flexibility. Each candlestick pattern is not only coded into the system but also comes with individual toggles to enable or disable them based on your trading strategy. This means you can choose to focus on single-candle patterns like Bullish Engulfing or more complex multi-candle formations such as Three White Soldiers, without modifying the core code. The built-in customization options allow you to adjust colors and labels for each pattern, giving you the freedom to tailor the visual output to your preference. This level of customization ensures that the indicator integrates seamlessly into your existing TradingView setup.
Moreover, the indicator isn’t just about pattern recognition—it also incorporates outcome-based learning. Every time a pattern is detected, it looks ahead a predefined number of bars to evaluate if the expected reversal actually materialized. This outcome is then stored in arrays, and over time, the script dynamically calculates the probability of success for each pattern. These probabilities are presented in a real-time updating table on your chart, which shows not only the percentage probability but also the count of historical occurrences. With this information at your fingertips, you can quickly gauge the reliability of each pattern in your chosen market and timeframe.
Another significant advantage of this approach is its speed and efficiency. While more complex machine learning models like neural networks might require heavy computational resources and longer training times, the Naive Bayes classifier in this script is lightweight, instantaneous and can be updated on the fly with each new bar. This real-time capability is essential for modern traders who need to make quick decisions in fast-paced markets.
Furthermore, by automating the process of backtesting, the indicator frees up your time to focus on other aspects of trading strategy development. Instead of manually analyzing hundreds or even thousands of candles, you can rely on the statistical power of Naive Bayes to provide you with insights on which patterns are most likely to result in profitable moves. This not only enhances your efficiency but also helps to eliminate the cognitive biases that often plague manual analysis.
In summary, this indicator represents a fusion of traditional candlestick analysis with modern machine learning techniques. It harnesses the simplicity and effectiveness of Naive Bayes to deliver a dynamic, real-time evaluation of various candlestick patterns. Whether you are a seasoned trader looking to refine your technical analysis or a beginner eager to understand market dynamics, this tool offers a powerful, customizable, and efficient solution. Welcome to a new era where advanced statistical methods meet practical trading insights—happy trading and may your patterns always be in your favor!
Note : On this current released beta version, you must manually adjust reversal percentage move based on each market. Further updates may include automated best range detection and probability.
Multi-indicator Signal Builder [Skyrexio]Overview
Multi-Indicator Signal Builder is a versatile, all-in-one script designed to streamline your trading workflow by combining multiple popular technical indicators under a single roof. It features a single-entry, single-exit logic, intrabar stop-loss/take-profit handling, an optional time filter, a visually accessible condition table, and a built-in statistics label. Traders can choose any combination of 12+ indicators (RSI, Ultimate Oscillator, Bollinger %B, Moving Averages, ADX, Stochastic, MACD, PSAR, MFI, CCI, Heikin Ashi, and a “TV Screener” placeholder) to form entry or exit conditions. This script aims to simplify strategy creation and analysis, making it a powerful toolkit for technical traders.
Indicators Overview
1. RSI (Relative Strength Index)
Measures recent price changes to evaluate overbought or oversold conditions on a 0–100 scale.
2. Ultimate Oscillator (UO)
Uses weighted averages of three different timeframes, aiming to confirm price momentum while avoiding false divergences.
3. Bollinger %B
Expresses price relative to Bollinger Bands, indicating whether price is near the upper band (overbought) or lower band (oversold).
4. Moving Average (MA)
Smooths price data over a specified period. The script supports both SMA and EMA to help identify trend direction and potential crossovers.
5. ADX (Average Directional Index)
Gauges the strength of a trend (0–100). Higher ADX signals stronger momentum, while lower ADX indicates a weaker trend.
6. Stochastic
Compares a closing price to a price range over a given period to identify momentum shifts and potential reversals.
7. MACD (Moving Average Convergence/Divergence)
Tracks the difference between two EMAs plus a signal line, commonly used to spot momentum flips through crossovers.
8. PSAR (Parabolic SAR)
Plots a trailing stop-and-reverse dot that moves with the trend. Often used to signal potential reversals when price crosses PSAR.
9. MFI (Money Flow Index)
Similar to RSI but incorporates volume data. A reading above 80 can suggest overbought conditions, while below 20 may indicate oversold.
10. CCI (Commodity Channel Index)
Identifies cyclical trends or overbought/oversold levels by comparing current price to an average price over a set timeframe.
11. Heikin Ashi
A type of candlestick charting that filters out market noise. The script uses a streak-based approach (multiple consecutive bullish or bearish bars) to gauge mini-trends.
12. TV Screener
A placeholder condition designed to integrate external buy/sell logic (like a TradingView “Buy” or “Sell” rating). Users can override or reference external signals if desired.
Unique Features
1. Multi-Indicator Entry and Exit
You can selectively enable any subset of 12+ classic indicators, each with customizable parameters and conditions. A position opens only if all enabled entry conditions are met, and it closes only when all enabled exit conditions are satisfied, helping reduce false triggers.
2. Single-Entry / Single-Exit with Intrabar SL/TP
The script supports a single position at a time. Once a position is open, it monitors intrabar to see if the price hits your stop-loss or take-profit levels before the bar closes, making results more realistic for fast-moving markets.
3. Time Window Filter
Users may specify a start/end date range during which trades are allowed, making it convenient to focus on specific market cycles for backtesting or live trading.
4. Condition Table and Statistics
A table at the bottom of the chart lists all active entry/exit indicators. Upon each closed trade, an integrated statistics label displays net profit, total trades, win/loss count, average and median PnL, etc.
5. Seamless Alerts and Automation
Configure alerts in TradingView using “Any alert() function call.”
The script sends JSON alert messages you can route to your own webhook.
The indicator can be integrated with Skyrexio alert bots to automate execution on major cryptocurrency exchanges
6. Optional MA/PSAR Plots
For added visual clarity, optionally plot the chosen moving averages or PSAR on the chart to confirm signals without stacking multiple indicators.
Methodology
1. Multi-Indicator Entry Logic
When multiple entry indicators are enabled (e.g., RSI + Stochastic + MACD), the script requires all signals to align before generating an entry. Each indicator can be set for crossovers, crossunders, thresholds (above/below), etc. This “AND” logic aims to filter out low-confidence triggers.
2. Single-Entry Intrabar SL/TP
One Position At a Time: Once an entry signal triggers, a trade opens at the bar’s close.
Intrabar Checks: Stop-loss and take-profit levels (if enabled) are monitored on every tick. If either is reached, the position closes immediately, without waiting for the bar to end.
3. Exit Logic
All Conditions Must Agree: If the trade is still open (SL/TP not triggered), then all enabled exit indicators must confirm a closure before the script exits on the bar’s close.
4. Time Filter
Optional Trading Window: You can activate a date/time range to constrain entries and exits strictly to that interval.
Justification of Methodology
Indicator Confluence: Combining multiple tools (RSI, MACD, etc.) can reduce noise and false signals.
Intrabar SL/TP: Capturing real-time spikes or dips provides a more precise reflection of typical live trading scenarios.
Single-Entry Model: Straightforward for both manual and automated tracking (especially important in bridging to bots).
Custom Date Range: Helps refine backtesting for specific market conditions or to avoid known irregular data periods.
How to Use
1. Add the Script to Your Chart
In TradingView, open Indicators , search for “Multi-indicator Signal Builder”.
Click to add it to your chart.
2. Configure Inputs
Time Filter: Set a start and end date for trades.
Alerts Messages: Input any JSON or text payload needed by your external service or bot.
Entry Conditions: Enable and configure any indicators (e.g., RSI, MACD) for a confluence-based entry.
Close Conditions: Enable exit indicators, along with optional SL (negative %) and TP (positive %) levels.
3. Set Up Alerts
In TradingView, select “Create Alert” → Condition = “Any alert() function call” → choose this script.
Entry Alert: Triggers on the script’s entry signal.
Close Alert: Triggers on the script’s close signal (or if SL/TP is hit).
Skyrexio Alert Bots: You can route these alerts via webhook to Skyrexio alert bots to automate order execution on major crypto exchanges (or any other supported broker).
4. Visual Reference
A condition table at the bottom summarizes active signals.
Statistics Label updates automatically as trades are closed, showing PnL stats and distribution metrics.
Backtesting Guidelines
Symbol/Timeframe: Works on multiple assets and timeframes; always do thorough testing.
Realistic Costs: Adjust commissions and potential slippage to match typical exchange conditions.
Risk Management: If using the built-in stop-loss/take-profit, set percentages that reflect your personal risk tolerance.
Longer Test Horizons: Verify performance across diverse market cycles to gauge reliability.
Example of statistic calculation
Test Period: 2023-01-01 to 2025-12-31
Initial Capital: $1,000
Commission: 0.1%, Slippage ~5 ticks
Trade Count: 468 (varies by strategy conditions)
Win rate: 76% (varies by strategy conditions)
Net Profit: +96.17% (varies by strategy conditions)
Disclaimer
This indicator is provided strictly for informational and educational purposes .
It does not constitute financial or trading advice.
Past performance never guarantees future results.
Always test thoroughly in demo environments before using real capital.
Enjoy exploring the Multi-Indicator Signal Builder! Experiment with different indicator combinations and adjust parameters to align with your trading preferences, whether you trade manually or link your alerts to external automation services. Happy trading and stay safe!
SPY/TLT Strategy█ STRATEGY OVERVIEW
The "SPY/TLT Strategy" is a trend-following crossover strategy designed to trade the relationship between TLT and its Simple Moving Average (SMA). The default configuration uses TLT (iShares 20+ Year Treasury Bond ETF) with a 20-period SMA, entering long positions on bullish crossovers and exiting on bearish crossunders. **This strategy is NOT optimized and performs best in trending markets.**
█ KEY FEATURES
SMA Crossover System: Uses price/SMA relationship for signal generation (Default: 20-period)
Dynamic Time Window: Configurable backtesting period (Default: 2014-2099)
Equity-Based Position Sizing: Default 100% equity allocation per trade
Real-Time Visual Feedback: Price/SMA plot with trend-state background coloring
Event-Driven Execution: Processes orders at bar close for accurate backtesting
█ SIGNAL GENERATION
1. LONG ENTRY CONDITION
TLT closing price crosses ABOVE SMA
Occurs within specified time window
Generates market order at next bar open
2. EXIT CONDITION
TLT closing price crosses BELOW SMA
Closes all open positions immediately
█ ADDITIONAL SETTINGS
SMA Period: Simple Moving Average length (Default: 20)
Start Time and End Time: The time window for trade execution (Default: 1 Jan 2014 - 1 Jan 2099)
Security Symbol: Ticker for analysis (Default: TLT)
█ PERFORMANCE OVERVIEW
Ideal Market Conditions: Strong trending environments
Potential Drawbacks: Whipsaws in range-bound markets
Backtesting results should be analyzed to optimize the MA Period and EMA Filter settings for specific instruments
CauchyTrend [InvestorUnknown]The CauchyTrend is an experimental tool that leverages a Cauchy-weighted moving average combined with a modified Supertrend calculation. This unique approach provides traders with insight into trend direction, while also offering an optional ATR-based range analysis to understand how often the market closes within, above, or below a defined volatility band.
Core Concepts
Cauchy Distribution and Gamma Parameter
The Cauchy distribution is a probability distribution known for its heavy tails and lack of a defined mean or variance. It is characterized by two parameters: a location parameter (x0, often 0 in our usage) and a scale parameter (γ, "gamma").
Gamma (γ): Determines the "width" or scale of the distribution. Smaller gamma values produce a distribution more concentrated near the center, giving more weight to recent data points, while larger gamma values spread the weight more evenly across the sample.
In this indicator, gamma influences how much emphasis is placed on values closer to the current price versus those further away in time. This makes the resulting weighted average either more reactive or smoother, depending on gamma’s value.
// Cauchy PDF formula used for weighting:
// f(x; γ) = (1/(π*γ)) *
f_cauchyPDF(offset, gamma) =>
numerator = gamma * gamma
denominator = (offset * offset) + (gamma * gamma)
pdf = (1 / (math.pi * gamma)) * (numerator / denominator)
pdf
A chart showing different Cauchy PDFs with various gamma values, illustrating how gamma affects the weight distribution.
Cauchy-Weighted Moving Average (CWMA)
Using the Cauchy PDF, we calculate normalized weights to create a custom Weighted Moving Average. Each bar in the lookback period receives a weight according to the Cauchy PDF. The result is a Cauchy Weighted Average (cwm_avg) that differs from typical moving averages, potentially offering unique sensitivity to price movements.
// Summation of weighted prices using Cauchy distribution weights
cwm_avg = 0.0
for i = 0 to length - 1
w_norm = array.get(weights, i) / sum_w
cwm_avg += array.get(values, i) * w_norm
Supertrend with a Cauchy Twist
The indicator integrates a modified Supertrend calculation using the cwm_avg as its reference point. The Supertrend logic typically sets upper and lower bands based on volatility (ATR), and flips direction when price crosses these bands.
In this case, the Cauchy-based average replaces the usual baseline, aiming to capture trend direction via a different weighting mechanism.
When price closes above the upper band, the trend is considered bullish; closing below the lower band signals a bearish trend.
ATR Stats Range (Optional)
Beyond the fundamental trend detection, the indicator optionally computes ATR-based stats to understand price distribution relative to a volatility corridor centered on the cwm_avg line:
Volatility Range:
Defined as cwm_avg ± (ATR * atr_mult), this range creates upper and lower bands. Turning on atr_stats computes how often the daily close falls: Within the range, Above the upper ATR boundary, Below the lower ATR boundary, Within the range but above cwm_avg, Within the range but below cwm_avg
These statistics can help traders gauge how the market behaves relative to this volatility envelope and possibly identify if the market tends to revert to the mean or break out more often.
Backtesting and Performance Metrics
The code is integrated with a backtesting library that allows users to assess strategy performance historically:
Equity Curve Calculation: Compares CauchyTrend-based signals against the underlying asset.
Performance Metrics Table: Once enabled, displays key metrics such as mean returns, Sharpe Ratio, Sortino Ratio, and more, comparing the strategy to a simple Buy & Hold approach.
Alerts and Notifications
The indicator provides Alerts for key events:
Long Alert: Triggered when the trend flips bullish.
Short Alert: Triggered when the trend flips bearish.
Customization and Calibration
Important: The default parameters are not optimized for any specific instrument or time frame. Traders should:
Adjust the length and gamma parameters to influence how sharply or broadly the cwm_avg reacts to price changes.
Tune the atr_len and atr_mult for the Supertrend logic to better match the asset’s volatility characteristics.
Experiment with atr_stats on/off to see if that additional volatility distribution information provides helpful insights.
Traders may find certain sets of parameters that align better with their preferred trading style, risk tolerance, or asset volatility profile.
Disclaimer: This indicator is for educational and informational purposes only. Past performance in backtesting does not guarantee future results. Always perform due diligence, and consider consulting a qualified financial advisor before trading.
AadTrend [InvestorUnknown]The AadTrend indicator is an experimental trading tool that combines a user-selected moving average with the Average Absolute Deviation (AAD) from this moving average. This combination works similarly to the Supertrend indicator but offers additional flexibility and insights. In addition to generating Long and Short signals, the AadTrend indicator identifies RISK-ON and RISK-OFF states for each trade direction, highlighting areas where taking on more risk may be considered.
Core Concepts and Features
Moving Average (User-Selected Type)
The indicator allows users to select from various types of moving averages to suit different trading styles and market conditions:
Simple Moving Average (SMA)
Exponential Moving Average (EMA)
Hull Moving Average (HMA)
Double Exponential Moving Average (DEMA)
Triple Exponential Moving Average (TEMA)
Relative Moving Average (RMA)
Fractal Adaptive Moving Average (FRAMA)
Average Absolute Deviation (AAD)
The Average Absolute Deviation measures the average distance between each data point and the mean, providing a robust estimation of volatility.
aad(series float src, simple int length, simple string avg_type) =>
avg = // Moving average as selected by the user
abs_deviations = math.abs(src - avg)
ta.sma(abs_deviations, length)
This provides a volatility measure that adapts to recent market conditions.
Combining Moving Average and AAD
The indicator creates upper and lower bands around the moving average using the AAD, similar to how the Supertrend indicator uses Average True Range (ATR) for its bands.
AadTrend(series float src, simple int length, simple float aad_mult, simple string avg_type) =>
// Calculate AAD (volatility measure)
aad_value = aad(src, length, avg_type)
// Calculate the AAD-based moving average by scaling the price data with AAD
avg = switch avg_type
"SMA" => ta.sma(src, length)
"EMA" => ta.ema(src, length)
"HMA" => ta.hma(src, length)
"DEMA" => ta.dema(src, length)
"TEMA" => ta.tema(src, length)
"RMA" => ta.rma(src, length)
"FRAMA" => ta.frama(src, length)
avg_p = avg + (aad_value * aad_mult)
avg_m = avg - (aad_value * aad_mult)
var direction = 0
if ta.crossover(src, avg_p)
direction := 1
else if ta.crossunder(src, avg_m)
direction := -1
A chart displaying the moving average with upper and lower AAD bands enveloping the price action.
Signals and Trade States
1. Long and Short Signals
Long Signal: Generated when the price crosses above the upper AAD band,
Short Signal: Generated when the price crosses below the lower AAD band.
2. RISK-ON and RISK-OFF States
These states provide additional insight into the strength of the current trend and potential opportunities for taking on more risk.
RISK-ON Long: When the price moves significantly above the upper AAD band after a Long signal.
RISK-OFF Long: When the price moves back below the upper AAD band, suggesting caution.
RISK-ON Short: When the price moves significantly below the lower AAD band after a Short signal.
RISK-OFF Short: When the price moves back above the lower AAD band.
Highlighted areas on the chart representing RISK-ON and RISK-OFF zones for both Long and Short positions.
A chart showing the filled areas corresponding to trend directions and RISK-ON zones
Backtesting and Performance Metrics
While the AadTrend indicator focuses on generating signals and highlighting risk areas, it can be integrated with backtesting frameworks to evaluate performance over historical data.
Integration with Backtest Library:
import InvestorUnknown/BacktestLibrary/1 as backtestlib
Customization and Calibration
1. Importance of Calibration
Default Settings Are Experimental: The default parameters are not optimized for any specific market condition or asset.
User Calibration: Traders should adjust the length, aad_mult, and avg_type parameters to align the indicator with their trading strategy and the characteristics of the asset being analyzed.
2. Factors to Consider
Market Volatility: Higher volatility may require adjustments to the aad_mult to avoid false signals.
Trading Style: Short-term traders might prefer faster-moving averages like EMA or HMA, while long-term traders might opt for SMA or FRAMA.
Alerts and Notifications
The AadTrend indicator includes built-in alert conditions to notify traders of significant market events:
Long and Short Alerts:
alertcondition(long_alert, "LONG (AadTrend)", "AadTrend flipped ⬆LONG⬆")
alertcondition(short_alert, "SHORT (AadTrend)", "AadTrend flipped ⬇Short⬇")
RISK-ON and RISK-OFF Alerts:
alertcondition(risk_on_long, "RISK-ON LONG (AadTrend)", "RISK-ON LONG (AadTrend)")
alertcondition(risk_off_long, "RISK-OFF LONG (AadTrend)", "RISK-OFF LONG (AadTrend)")
alertcondition(risk_on_short, "RISK-ON SHORT (AadTrend)", "RISK-ON SHORT (AadTrend)")
alertcondition(risk_off_short, "RISK-OFF SHORT (AadTrend)", "RISK-OFF SHORT (AadTrend)")
Important Notes and Disclaimer
Experimental Nature: The AadTrend indicator is experimental and should be used with caution.
No Guaranteed Performance: Past performance is not indicative of future results. Backtesting results may not reflect real trading conditions.
User Responsibility: Traders and investors should thoroughly test and calibrate the indicator settings before applying it to live trading.
Risk Management: Always use proper risk management techniques, including stop-loss orders and position sizing.
MadTrend [InvestorUnknown]The MadTrend indicator is an experimental tool that combines the Median and Median Absolute Deviation (MAD) to generate signals, much like the popular Supertrend indicator. In addition to identifying Long and Short positions, MadTrend introduces RISK-ON and RISK-OFF states for each trade direction, providing traders with nuanced insights into market conditions.
Core Concepts
Median and Median Absolute Deviation (MAD)
Median: The middle value in a sorted list of numbers, offering a robust measure of central tendency less affected by outliers.
Median Absolute Deviation (MAD): Measures the average distance between each data point and the median, providing a robust estimation of volatility.
Supertrend-like Functionality
MadTrend utilizes the median and MAD in a manner similar to how Supertrend uses averages and volatility measures to determine trend direction and potential reversal points.
RISK-ON and RISK-OFF States
RISK-ON: Indicates favorable conditions for entering or holding a position in the current trend direction.
RISK-OFF: Suggests caution, signaling RISK-ON end and potential trend weakening or reversal.
Calculating MAD
The mad function calculates the median of the absolute deviations from the median, providing a robust measure of volatility.
// Function to calculate the Median Absolute Deviation (MAD)
mad(series float src, simple int length) =>
med = ta.median(src, length) // Calculate median
abs_deviations = math.abs(src - med) // Calculate absolute deviations from median
ta.median(abs_deviations, length) // Return the median of the absolute deviations
MADTrend Function
The MADTrend function calculates the median and MAD-based upper (med_p) and lower (med_m) bands. It determines the trend direction based on price crossing these bands.
MADTrend(series float src, simple int length, simple float mad_mult) =>
// Calculate MAD (volatility measure)
mad_value = mad(close, length)
// Calculate the MAD-based moving average by scaling the price data with MAD
median = ta.median(close, length)
med_p = median + (mad_value * mad_mult)
med_m = median - (mad_value * mad_mult)
var direction = 0
if ta.crossover(src, med_p)
direction := 1
else if ta.crossunder(src, med_m)
direction := -1
Trend Direction and Signals
Long Position (direction = 1): When the price crosses above the upper MAD band (med_p).
Short Position (direction = -1): When the price crosses below the lower MAD band (med_m).
RISK-ON: When the price moves further in the direction of the trend (beyond median +- MAD) after the initial signal.
RISK-OFF: When the price retraces towards the median, signaling potential weakening of the trend.
RISK-ON and RISK-OFF States
RISK-ON LONG: Price moves above the upper band after a Long signal, indicating strengthening bullish momentum.
RISK-OFF LONG: Price falls back below the upper band, suggesting potential weakness in the bullish trend.
RISK-ON SHORT: Price moves below the lower band after a Short signal, indicating strengthening bearish momentum.
RISK-OFF SHORT: Price rises back above the lower band, suggesting potential weakness in the bearish trend.
Picture below show example RISK-ON periods which can be identified by “cloud”
Note: Highlighted areas on the chart indicating RISK-ON and RISK-OFF periods for both Long and Short positions.
Implementation Details
Inputs and Parameters:
Source (input_src): The price data used for calculations (e.g., close, open, high, low).
Median Length (length): The number of periods over which the median and MAD are calculated.
MAD Multiplier (mad_mult): Determines the distance of the upper and lower bands from the median.
Calculations:
Median and MAD are recalculated each period based on the specified length.
Upper (med_p) and Lower (med_m) Bands are computed by adding and subtracting the scaled MAD from the median.
Visual representation of the indicator on a price chart:
Backtesting and Performance Metrics
The MadTrend indicator includes a Backtesting Mode with a performance metrics table to evaluate its effectiveness compared to a simple buy-and-hold strategy.
Equity Calculation:
Calculates the equity curve based on the signals generated by the indicator.
Performance Metrics:
Metrics such as Mean Returns, Standard Deviation, Sharpe Ratio, Sortino Ratio, and Omega Ratio are computed.
The metrics are displayed in a table for both the strategy and the buy-and-hold approach.
Note: Due to the use of labels and plot shapes, automatic chart scaling may not function ideally in Backtest Mode.
Alerts and Notifications
MadTrend provides alert conditions to notify traders of significant events:
Trend Change Alerts
RISK-ON and RISK-OFF Alerts - Provides real-time notifications about the RISK-ON and RISK-OFF states for proactive trade management.
Customization and Calibration
Default Settings: The provided default settings are experimental and not optimized. They serve as a starting point for users.
Parameter Adjustment: Traders are encouraged to calibrate the indicator's parameters (e.g., length, mad_mult) to suit their specific trading style and the characteristics of the asset being analyzed.
Source Input: The indicator allows for different price inputs (open, high, low, close, etc.), offering flexibility in how the median and MAD are calculated.
Important Notes
Market Conditions: The effectiveness of the MadTrend indicator can vary across different market conditions. Regular calibration is recommended.
Backtest Limitations: Backtesting results are historical and do not guarantee future performance.
Risk Management: Always apply sound risk management practices when using any trading indicator.
