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Laguerre RSI by KivancOzbilgic STRATEGYBacktesting. " Laguerre RSI is based on John EHLERS' Laguerre Filter to avoid the noise of RSI . Change alpha coefficient to increase/decrease lag and smoothness. Buy when Laguerre RSI crosses upwards above 20. Sell when Laguerre RSI crosses down below 80. While indicator runs flat above 80 level, it means that an uptrend is strong. While indicator runs flat below 20 level, it means that a downtrend is strong. " Developer: John EHLERS Author: KivancOzbilgic
Pine Script™ 전략
mertriver1의
8
RSI-VWAPBacktest script based on the previous RSI-VWAP indicator: It's the popular RSI indicator with VWAP as a source instead of close: - RSI_VWAP = rsi(vwap(close), RSI_VWAP_length) What is the Volume Weighted Average Price ( VWAP )? VWAP is calculated by adding up the dollars traded for every transaction (price multiplied by the number of shares traded) and then dividing by the total shares traded. Trades are laddered to improve the average entry price and each entry is increased, improving the entry but increasing the risk of being liquidated. It can be easily converted to study (alerts) Settings for BINANCE:BTCUSDT at 30m
Pine Script™ 전략
UnknownUnicorn2151907의
29
Smart Scalper Pro Template + VWAP 📌 Author Garry Evans Independent system developer focused on: Risk-first automation Market structure & liquidity behavior Discipline, consistency, and capital preservation “The edge isn’t the market — it’s the man who survives it.” ⚙️ Risk Management & Position Sizing The script is built around capital protection, not signal frequency. Risk logic includes: Fixed or dynamic risk per trade Market-adaptive position sizing Session-based trade limits Daily trade caps and auto-lockout protection Volatility-aware sizing (futures & crypto) ⚠️ Profit is pursued only after risk is controlled. 📊 Track Record Backtested across multiple market environments Forward-tested and actively used by the author Real-account trades are logged where platform rules allow Results vary by market, timeframe, and user-defined risk settings. 🌍 Supported Markets Designed to work across all liquid markets, including: Stocks Crypto (spot & futures) Options (signal-based framework) Futures (indices, metals, crypto futures) The system adapts to volatility and structure — it is not market-specific. ⚖️ Leverage Leverage is not required If used, leverage is fully user-controlled Risk logic scales exposure conservatively No martingale. No revenge sizing. No over-exposure logic. 🧪 Backtesting ✔ Yes Strategy logic has been backtested Filters reduce chop, noise, and forced trades Focus on drawdown control over curve-fitting 🛠 Support ✔ Yes Direct author support Ongoing improvements and updates Feature refinement based on real usage and feedback 👥 Community ✔ Yes Private user access High-quality feedback environment No public signal spam or hype-driven chat rooms ⏳ Trial Period ✔ Yes Limited trial access available Designed for evaluation only Trial users do not receive full feature access 🚫 Who This Script Is NOT For This system is not for: Traders looking for guaranteed profits Users expecting copy-paste “signal calls” Over-leveraged gamblers Those unwilling to follow risk rules Anyone seeking overnight results This is a discipline and automation tool, not a shortcut. 🧠 Final Positioning This is not a signal service. This is a risk-controlled execution framework designed to: Enforce discipline Reduce emotional trading Protect capital during bad market conditions Scale responsibly during favorable ones
Pine Script® 인디케이터
muscletwins954305의
Risk Manager & ATR TS Strategy📌 Overview This script is not a simple indicator mashup. It is a Risk & Trade Planning Engine that combines a strategy-based signal generator with a snapshot-based risk, sizing, and expectancy model. It is designed to support real trading decisions, not just to generate cosmetic signals or overfitted backtests. The core idea is to separate market logic from risk logic, evaluating each trade only at the moment it becomes actionable using fixed reference points that do not change afterward. 🎯 What makes this script original Unlike most tools that merely combine indicators or visualize entries, this script introduces several non-standard design choices: Snapshot-based risk sizing (The "Time Machine" logic). Expected Value (EV) calculation in both Money and R-multiples. Kelly Criterion applied with weighted multi-target logic. Strict architectural separation between the signal engine and the risk engine. Decision-oriented dashboard instead of decorative plots. These components are not merged for convenience; they are architecturally dependent on each other. 🧠 Conceptual Architecture 1️⃣ Signal Engine (Market Context) The signal engine is based on an ATR Trailing Stop system combined with trend regime filters (ADX and Choppiness Index). Its only responsibility is to answer one question: "Is this a valid directional opportunity right now?" It does not manage risk; it only identifies the opportunity. 2️⃣ Snapshot Logic (Key Design Choice) When a valid signal occurs, the script captures a Snapshot of the Entry price, Initial Stop-Loss, and Risk Distance. This snapshot is frozen at signal time. It is never updated, even if the trailing stop moves later. This avoids the most common error in TradingView scripts: recalculating position size using a moving stop, which falsifies the risk data. 3️⃣ Risk Engine (Sizing & Control) Using the snapshot values, the script computes: Monetary risk per trade (capped at your user-defined max). Position size derived from the fixed stop distance. Effective leverage (informational). 4️⃣ Multi-Target Reward Model Instead of assuming a single take-profit, the script supports multiple targets with user-defined probability weights. From this, it derives a Weighted Risk/Reward Ratio, which feeds directly into the EV and Kelly calculations. 5️⃣ Expected Value (EV) in Money & R The script calculates EV in your account currency (real impact) and normalized in R-multiples (statistical quality). This allows you to compare trade quality across different assets and timeframes objectively. 6️⃣ Kelly Criterion (Conservative) The Kelly Criterion is applied using the weighted reward model and is always subordinated to your hard risk cap. If Kelly suggests a negative value, the script advises "NO TRADE". It is used as a filter, not a leverage amplifier. 📊 Dashboard & Alerts The on-chart dashboard summarizes everything you need at the moment of the signal: Risk % and Position Size Expected Value (Money + R) Kelly Suggestion Signal Strength Alerts are triggered once per signal (on bar close) using snapshot data, ensuring no repainting and no spam. 🔍 How this is NOT a mashup Each component exists because another component depends on it. Snapshot logic is required for valid risk sizing; Risk sizing is required for EV normalization; Weighted RR is required for meaningful Kelly. Removing any part breaks the system’s logic. 📘 How to use Choose your account size and risk parameters in the settings. Configure your stop logic and reward targets. Wait for a valid signal. Evaluate the dashboard: Decide if the trade quality (EV, R, Risk) justifies participation. ⚖️ Open-Source Notice This script is published under the Mozilla Public License 2.0 (MPL-2.0). It does not copy or replicate any single public script. Standard concepts (ATR, ADX) are used as building blocks, but the architecture and calculations are original. 🚫 Disclaimer This script is a planning and evaluation engine designed to help traders think in terms of risk, expectancy, and discipline. It does not guarantee profitability. ✅ Summary This is a professional-grade framework built to answer one core question: “Is this trade worth taking, given my risk and my expectations?” Not every signal is a trade, and not every trade deserves capital. This script helps you make that distinction.
Pine Script® 인디케이터
mentalExpert19609의
DayTradeMind Combined High Win Rate StrategyThe DayTradeMind Combined High Win Rate Strategy is a trend-following system that relies on confluence—the idea that a trade signal is stronger when multiple independent indicators agree. Instead of entering on a single indicator's whim, it uses a "voting" system to qualify entries and a strict risk-to-reward ratio to manage exits.Here is a breakdown of the three main layers of this strategy:1. The Voting Engine (Confluence Model)The strategy tracks four indicators and assigns a "point" for a bullish or bearish bias. It requires a minimum number of points (set by minConfirmations, usually 2/4) before it even considers a trade.IndicatorBullish Condition (1 point)Bearish Condition (1 point)PurposeMACDMACD Line > Signal LineMACD Line < Signal LineMeasures short-term momentum.DonchianPrice > 20-period MedianPrice < 20-period MedianIdentifies price relative to recent range.SuperTrendPrice above trend linePrice below trend lineFilters for the "Macro" trend direction.%B (Bollinger)Price in lower-mid range (0.2–0.5)Price in upper-mid range (0.5–0.8)Prevents buying when overextended.2. The Entry TriggerHaving enough "votes" (confirmations) isn't enough to enter. The strategy waits for a trigger event to ensure you aren't entering a stale trend. An entry only occurs if the minimum confirmations are met AND one of the following happens on the current bar:MACD Cross: The MACD line crosses over the signal line.Structural Break: The price crosses over the Donchian Middle (Median) line.This "Confirmation + Trigger" approach is designed to catch the start of a momentum push rather than buying a flat market.3. Mathematical Risk ManagementThe performance you see in your backtest (like the 46.86% return) is largely driven by the 2:1 Reward-to-Risk (RR) Ratio.Stop Loss (SL): Fixed at 2% below entry.Take Profit (TP): Fixed at 4% above entry.By aiming for a target twice as large as the risk, the strategy can remain profitable even with a win rate as low as 35%–40%. Mathematically, your winning trades compensate for more than two losing trades.Visualizing the SystemTriangles: Small green (up) and red (down) triangles appear on your chart only when the Votes + Trigger align perfectly.Background Shading: Faint green or red bands show you exactly when the "Confluence" is active. If the background is gray, the indicators are in conflict.Dashboard: The table in the top-right summarizes the current "score" for each indicator, letting you know how close you are to a potential trade signal.
Pine Script™ 전략
day_trade_mind의
XAUUSD Mean Reversion Strategy Gold (ATR and RSI)The XAUUSD Mean Reversion Strategy – Gold v6 is a non-repainting TradingView strategy designed specifically for Gold (XAUUSD). It capitalizes on price overextensions and statistically probable pullbacks toward the mean, a behavior Gold frequently exhibits during active market sessions. 🔍 Strategy Logic Uses EMA 50 as the mean price reference Detects overextended conditions with RSI (14) Trades are taken only when price deviates significantly from the mean Designed for both long and short positions 📈 Entry Conditions Long Trades Price below EMA 50 RSI below oversold level Short Trades Price above EMA 50 RSI above overbought level 📉 Exit & Risk Management ATR-based Stop Loss adapts to Gold’s volatility Take Profit Options Mean reversion back to EMA Fixed ATR-based risk-to-reward One trade at a time to control exposure ⚙️ Features Fully backtestable Non-repainting Optimized for XAUUSD volatility Adjustable inputs for optimization Works best on 5m–30m timeframes 📊 Recommended Use XAUUSD (Gold) London & New York sessions Intraday mean-reversion traders ⚠️ This strategy is for educational and research purposes only. Always perform your own testing and risk management before using it in live markets.
Pine Script™ 전략
JaxonJackFX의
Asset Rotation System [InvestorUnknown]Overview This system creates a comprehensive trend "matrix" by analyzing the performance of six assets against both the US Dollar and each other. The objective is to identify and hold the asset that is currently outperforming all others, thereby focusing on maintaining an investment in the most "optimal" asset at any given time. - - - Key Features - - - 1. Trend Classification: The system evaluates the trend for each of the six assets, both individually against USD and in pairs (assetX/assetY), to determine which asset is currently outperforming others. Utilizes five distinct trend indicators: RSI (50 crossover), CCI, SuperTrend, DMI, and Parabolic SAR. Users can customize the trend analysis by selecting all indicators or choosing a single one via the "Trend Classification Method" input setting. 2. Backtesting: Calculates an equity curve for each asset and for the system itself, which assumes holding only the asset deemed optimal at any time. Customizable start date for backtesting; by default, it begins either 5000 bars ago (the maximum in TradingView) or at the inception of the youngest asset included, whichever is shorter. If the youngest asset's history exceeds 5000 bars, the system uses 5000 bars to prevent errors. The equity curve is dynamically colored based on the asset held at each point, with this coloring also reflected on the chart via barcolor(). Performance metrics like returns, standard deviation of returns, Sharpe, Sortino, and Omega ratios, along with maximum drawdown, are computed for each asset and the system's equity curve. 3 Alerts: Supports alerts for when a new, confirmed optimal asset is identified. However, due to TradingView limitations, the specific asset cannot be included in the alert message. - - - Usage - - - 1. Select Assets/Tickers: Choose which assets or tickers you want to include in the rotation system. Ensure that all selected tickers are denominated in USD to maintain consistency in analysis. 2. Configure Trend Classification: Decide on the trend classification method from the available options (RSI, CCI, SuperTrend, DMI, or Parabolic SAR, All) and adjust the settings to your preferences. This customization allows you to tailor the system to different market conditions or your specific trading strategy. 3. Utilize Backtesting for Calibration: Use the backtesting results, including equity curves and performance metrics, to fine-tune your chosen trend indicators. Be cautious not to overemphasize performance maximization, as this can lead to overfitting. The goal is to achieve a robust system that performs well across various market conditions, rather than just optimizing for past data. - - - Parameters - - - Tickers: Asset 1: Select the symbol for the first asset. Asset 2: Select the symbol for the second asset. Asset 3: Select the symbol for the third asset. Asset 4: Select the symbol for the fourth asset. Asset 5: Select the symbol for the fifth asset. Asset 6: Select the symbol for the sixth asset. General Settings: Trend Classification Method: Choose from RSI, CCI, SuperTrend, DMI, PSAR, or "All" to determine how trends are analyzed. Use Custom Starting Date for Backtest: Toggle to use a custom date for beginning the backtest. Custom Starting Date: Set the custom start date for backtesting. Plot Perf. Metrics Table: Option to display performance metrics in a table on the chart. RSI (Relative Strength Index): RSI Source: Choose the price data source for RSI calculation. RSI Length: Set the period for the RSI calculation. CCI (Commodity Channel Index): CCI Source: Select the price data source for CCI calculation. CCI Length: Determine the period for the CCI. SuperTrend: SuperTrend Factor: Adjust the sensitivity of the SuperTrend indicator. SuperTrend Length: Set the period for the SuperTrend calculation. DMI (Directional Movement Index): DMI Length: Define the period for DMI calculations. Parabolic SAR: PSAR Start: Initial acceleration factor for the Parabolic SAR. PSAR Increment: Increment value for the acceleration factor. PSAR Max Value: Maximum value the acceleration factor can reach. Notes/Recommendations: While this system is operational, it's important to recognize that it relies on "basic" indicators, which may not be ideal for generating trading signals on their own. I strongly suggest that users delve into the code to grasp the underlying logic of the system. Consider customizing it by integrating more sophisticated and higher-quality trend-following indicators to enhance its performance and reliability. Disclaimer: This system's backtest results are historical and do not predict future performance. Use for educational purposes only; not investment advice.
Pine Script® 인디케이터
InvestorUnknown의
28
Range Oscillator Strategy + Stoch Confirm🔹 Short summary This is a free, educational long-only strategy built on top of the public “Range Oscillator” by Zeiierman (used under CC BY-NC-SA 4.0), combined with a Stochastic timing filter, an EMA-based exit filter and an optional risk-management layer (SL/TP and R-multiple exits). It is NOT financial advice and it is NOT a magic money machine. It’s a structured framework to study how range-expansion + momentum + trend slope can be combined into one rule-based system, often with intentionally RARE trades. ──────────────────────── 0. Legal / risk disclaimer ──────────────────────── • This script is FREE and public. I do not charge any fee for it. • It is for EDUCATIONAL PURPOSES ONLY. • It is NOT financial advice and does NOT guarantee profits. • Backtest results can be very different from live results. • Markets change over time; past performance is NOT indicative of future performance. • You are fully responsible for your own trades and risk. Please DO NOT use this script with money you cannot afford to lose. Always start in a demo / paper trading environment and make sure you understand what the logic does before you risk any capital. ──────────────────────── 1. About default settings and risk (very important) ──────────────────────── The script is configured with the following defaults in the `strategy()` declaration: • `initial_capital = 10000` → This is only an EXAMPLE account size. • `default_qty_type = strategy.percent_of_equity` • `default_qty_value = 100` → This means 100% of equity per trade in the default properties. → This is AGGRESSIVE and should be treated as a STRESS TEST of the logic, not as a realistic way to trade. TradingView’s House Rules recommend risking only a small part of equity per trade (often 1–2%, max 5–10% in most cases). To align with these recommendations and to get more realistic backtest results, I STRONGLY RECOMMEND you to: 1. Open **Strategy Settings → Properties**. 2. Set: • Order size: **Percent of equity** • Order size (percent): e.g. **1–2%** per trade 3. Make sure **commission** and **slippage** match your own broker conditions. • By default this script uses `commission_value = 0.1` (0.1%) and `slippage = 3`, which are reasonable example values for many crypto markets. If you choose to run the strategy with 100% of equity per trade, please treat it ONLY as a stress-test of the logic. It is NOT a sustainable risk model for live trading. ──────────────────────── 2. What this strategy tries to do (conceptual overview) ──────────────────────── This is a LONG-ONLY strategy designed to explore the combination of: 1. **Range Oscillator (Zeiierman-based)** - Measures how far price has moved away from an adaptive mean. - Uses an ATR-based range to normalize deviation. - High positive oscillator values indicate strong price expansion away from the mean in a bullish direction. 2. **Stochastic as a timing filter** - A classic Stochastic (%K and %D) is used. - The logic requires %K to be below a user-defined level and then crossing above %D. - This is intended to catch moments when momentum turns up again, rather than chasing every extreme. 3. **EMA Exit Filter (trend slope)** - An EMA with configurable length (default 70) is calculated. - The slope of the EMA is monitored: when the slope turns negative while in a long position, and the filter is enabled, it triggers an exit condition. - This acts as a trend-protection exit: if the medium-term trend starts to weaken, the strategy exits even if the oscillator has not yet fully reverted. 4. **Optional risk-management layer** - Percentage-based Stop Loss and Take Profit (SL/TP). - Risk/Reward (R-multiple) exit based on the distance from entry to SL. - Implemented as OCO orders that work *on top* of the logical exits. The goal is not to create a “holy grail” system but to serve as a transparent, configurable framework for studying how these concepts behave together on different markets and timeframes. ──────────────────────── 3. Components and how they work together ──────────────────────── (1) Range Oscillator (based on “Range Oscillator (Zeiierman)”) • The script computes a weighted mean price and then measures how far price deviates from that mean. • Deviation is normalized by an ATR-based range and expressed as an oscillator. • When the oscillator is above the **entry threshold** (default 100), it signals a strong move away from the mean in the bullish direction. • When it later drops below the **exit threshold** (default 30), it can trigger an exit (if enabled). (2) Stochastic confirmation • Classic Stochastic (%K and %D) is calculated. • An entry requires: - %K to be below a user-defined “Cross Level”, and - then %K to cross above %D. • This is a momentum confirmation: the strategy tries to enter when momentum turns up from a pullback rather than at any random point. (3) EMA Exit Filter • The EMA length is configurable via `emaLength` (default 70). • The script monitors the EMA slope: it computes the relative change between the current EMA and the previous EMA. • If the slope turns negative while the strategy holds a long position and the filter is enabled, it triggers an exit condition. • This is meant to help protect profits or cut losses when the medium-term trend starts to roll over, even if the oscillator conditions are not (yet) signalling exit. (4) Risk management (optional) • Stop Loss (SL) and Take Profit (TP): - Defined as percentages relative to average entry price. - Both are disabled by default, but you can enable them in the Inputs. • Risk/Reward Exit: - Uses the distance from entry to SL to project a profit target at a configurable R-multiple. - Also optional and disabled by default. These exits are implemented as `strategy.exit()` OCO orders and can close trades independently of oscillator/EMA conditions if hit first. ──────────────────────── 4. Entry & Exit logic (high level) ──────────────────────── A) Time filter • You can choose a **Start Year** in the Inputs. • Only candles between the selected start date and 31 Dec 2069 are used for backtesting (`timeCondition`). • This prevents accidental use of tiny cherry-picked windows and makes tests more honest. B) Entry condition (long-only) A long entry is allowed when ALL the following are true: 1. `timeCondition` is true (inside the backtest window). 2. If `useOscEntry` is true: - Range Oscillator value must be above `entryLevel`. 3. If `useStochEntry` is true: - Stochastic condition (`stochCondition`) must be true: - %K < `crossLevel`, then %K crosses above %D. If these filters agree, the strategy calls `strategy.entry("Long", strategy.long)`. C) Exit condition (logical exits) A position can be closed when: 1. `timeCondition` is true AND a long position is open, AND 2. At least one of the following is true: - If `useOscExit` is true: Oscillator is below `exitLevel`. - If `useMagicExit` (EMA Exit Filter) is true: EMA slope is negative (`isDown = true`). In that case, `strategy.close("Long")` is called. D) Risk-management exits While a position is open: • If SL or TP is enabled: - `strategy.exit("Long Risk", ...)` places an OCO stop/limit order based on the SL/TP percentages. • If Risk/Reward exit is enabled: - `strategy.exit("RR Exit", ...)` places an OCO order using a projected R-multiple (`rrMult`) of the SL distance. These risk-based exits can trigger before the logical oscillator/EMA exits if price hits those levels. ──────────────────────── 5. Recommended backtest configuration (to avoid misleading results) ──────────────────────── To align with TradingView House Rules and avoid misleading backtests: 1. **Initial capital** - 10 000 (or any value you personally want to work with). 2. **Order size** - Type: **Percent of equity** - Size: **1–2%** per trade is a reasonable starting point. - Avoid risking more than 5–10% per trade if you want results that could be sustainable in practice. 3. **Commission & slippage** - Commission: around 0.1% if that matches your broker. - Slippage: a few ticks (e.g. 3) to account for real fills. 4. **Timeframe & markets** - Volatile symbols (e.g. crypto like BTCUSDT, or major indices). - Timeframes: 1H / 4H / **1D (Daily)** are typical starting points. - I strongly recommend trying the strategy on **different timeframes**, for example 1D, to see how the behaviour changes between intraday and higher timeframes. 5. **No “caution warning”** - Make sure your chosen symbol + timeframe + settings do not trigger TradingView’s caution messages. - If you see warnings (e.g. “too few trades”), adjust timeframe/symbol or the backtest period. ──────────────────────── 5a. About low trade count and rare signals ──────────────────────── This strategy is intentionally designed to trade RARELY: • It is **long-only**. • It uses strict filters (Range Oscillator threshold + Stochastic confirmation + optional EMA Exit Filter). • On higher timeframes (especially **1D / Daily**) this can result in a **low total number of trades**, sometimes WELL BELOW 100 trades over the whole backtest. TradingView’s House Rules mention 100+ trades as a guideline for more robust statistics. In this specific case: • The **low trade count is a conscious design choice**, not an attempt to cherry-pick a tiny, ultra-profitable window. • The goal is to study a **small number of high-conviction long entries** on higher timeframes, not to generate frequent intraday signals. • Because of the low trade count, results should NOT be interpreted as statistically strong or “proven” – they are only one sample of how this logic would have behaved on past data. Please keep this in mind when you look at the equity curve and performance metrics. A beautiful curve with only a handful of trades is still just a small sample. ──────────────────────── 6. How to use this strategy (step-by-step) ──────────────────────── 1. Add the script to your chart. 2. Open the **Inputs** tab: - Set the backtest start year. - Decide whether to use Oscillator-based entry/exit, Stochastic confirmation, and EMA Exit Filter. - Optionally enable SL, TP, and Risk/Reward exits. 3. Open the **Properties** tab: - Set a realistic account size if you want. - Set order size to a realistic % of equity (e.g. 1–2%). - Confirm that commission and slippage are realistic for your broker. 4. Run the backtest: - Look at Net Profit, Max Drawdown, number of trades, and equity curve. - Remember that a low trade count means the statistics are not very strong. 5. Experiment: - Tweak thresholds (`entryLevel`, `exitLevel`), Stochastic settings, EMA length, and risk params. - See how the metrics and trade frequency change. 6. Forward-test: - Before using any idea in live trading, forward-test on a demo account and observe behaviour in real time. ──────────────────────── 7. Originality and usefulness (why this is more than a mashup) ──────────────────────── This script is not intended to be a random visual mashup of indicators. It is designed as a coherent, testable strategy with clear roles for each component: • Range Oscillator: - Handles mean vs. range-expansion states via an adaptive, ATR-normalized metric. • Stochastic: - Acts as a timing filter to avoid entering purely on extremes and instead waits for momentum to turn. • EMA Exit Filter: - Trend-slope-based safety net to exit when the medium-term direction changes against the position. • Risk module: - Provides practical, rule-based exits: SL, TP, and R-multiple exit, which are useful for structuring risk even if you modify the core logic. It aims to give traders a ready-made **framework to study and modify**, not a black box or “signals” product. ──────────────────────── 8. Limitations and good practices ──────────────────────── • No single strategy works on all markets or in all regimes. • This script is long-only; it does not short the market. • Performance can degrade when market structure changes. • Overfitting (curve fitting) is a real risk if you endlessly tweak parameters to maximise historical profit. Good practices: - Test on multiple symbols and timeframes. - Focus on stability and drawdown, not only on how high the profit line goes. - View this as a learning tool and a basis for your own research. ──────────────────────── 9. Licensing and credits ──────────────────────── • Core oscillator idea & base code: - “Range Oscillator (Zeiierman)” - © Zeiierman, licensed under CC BY-NC-SA 4.0. • Strategy logic, Stochastic confirmation, EMA Exit Filter, and risk-management layer: - Modifications by jokiniemi. Please respect both the original license and TradingView House Rules if you fork or republish any part of this script. ──────────────────────── 10. No payments / no vendor pitch ──────────────────────── • This script is completely FREE to use on TradingView. • There is no paid subscription, no external payment link, and no private signals group attached to it. • If you have questions, please use TradingView’s comment system or private messages instead of expecting financial advice. Use this script as a tool to learn, experiment, and build your own understanding of markets. ──────────────────────── 11. Example backtest settings used in screenshots ──────────────────────── To avoid any confusion about how the results shown in screenshots were produced, here is one concrete example configuration: • Symbol: BTCUSDT (or similar major BTC pair) • Timeframe: 1D (Daily) • Backtest period: from 2018 to the most recent data • Initial capital: 10 000 • Order size type: Percent of equity • Order size: 2% per trade • Commission: 0.1% • Slippage: 3 ticks • Risk settings: Stop Loss and Take Profit disabled by default, Risk/Reward exit disabled by default • Filters: Range Oscillator entry/exit enabled, Stochastic confirmation enabled, EMA Exit Filter enabled If you change any of these settings (symbol, timeframe, risk per trade, commission, slippage, filters, etc.), your results will look different. Please always adapt the configuration to your own risk tolerance, market, and trading style.
Pine Script™ 전략
jokiniemi의
Smart DCA Strategy (Public)INSPIRATION While Dollar Cost Averaging (DCA) is a popular and stress-free investment approach, I noticed an opportunity for enhancement. Standard DCA involves buying consistently, regardless of market conditions, which can sometimes mean missing out on optimal investment opportunities. This led me to develop the Smart DCA Strategy – a 'set and forget' method like traditional DCA, but with an intelligent twist to boost its effectiveness. The goal was to build something more profitable than a standard DCA strategy so it was equally important that this indicator could backtest its own results in an A/B test manner against the regular DCA strategy. WHY IS IT SMART? The key to this strategy is its dynamic approach: buying aggressively when the market shows signs of being oversold, and sitting on the sidelines when it's not. This approach aims to optimize entry points, enhancing the potential for better returns while maintaining the simplicity and low stress of DCA. WHAT THIS STRATEGY IS, AND IS NOT This is an investment style strategy. It is designed to improve upon the common standard DCA investment strategy. It is therefore NOT a day trading strategy. Feel free to experiment with various timeframes, but it was designed to be used on a daily timeframe and that's how I recommend it to be used. You may also go months without any buy signals during bull markets, but remember that is exactly the point of the strategy - to keep your buying power on the sidelines until the markets have significantly pulled back. You need to be patient and trust in the historical backtesting you have performed. HOW IT WORKS The Smart DCA Strategy leverages a creative approach to using Moving Averages to identify the most opportune moments to buy. A trigger occurs when a daily candle, in its entirety including the high wick, closes below the threshold line or box plotted on the chart. The indicator is designed to facilitate both backtesting and live trading. HOW TO USE Settings: The input parameters for tuning have been intentionally simplified in an effort to prevent users falling into the overfitting trap. The main control is the Buying strictness scale setting. Setting this to a lower value will provide more buying days (less strict) while higher values mean less buying days (more strict). In my testing I've found level 9 to provide good all round results. Validation days is a setting to prevent triggering entries until the asset has spent a given number of days (candles) in the overbought state. Increasing this makes entries stricter. I've found 0 to give the best results across most assets. In the backtest settings you can also configure how much to buy for each day an entry triggers. Blind buy size is the amount you would buy every day in a standard DCA strategy. Smart buy size is the amount you would buy each day a Smart DCA entry is triggered. You can also experiment with backtesting your strategy over different historical datasets by using the Start date and End date settings. The results table will not calculate for any trades outside what you've set in the date range settings. Backtesting: When backtesting you should use the results table on the top right to tune and optimise the results of your strategy. As with all backtests, be careful to avoid overfitting the parameters. It's better to have a setup which works well across many currencies and historical periods than a setup which is excellent on one dataset but bad on most others. This gives a much higher probability that it will be effective when you move to live trading. The results table provides a clear visual representation as to which strategy, standard or smart, is more profitable for the given dataset. You will notice the columns are dynamically coloured red and green. Their colour changes based on which strategy is more profitable in the A/B style backtest - green wins, red loses. The key metrics to focus on are GOA (Gain on Account) and Avg Cost. Live Trading: After you've finished backtesting you can proceed with configuring your alerts for live trading. But first, you need to estimate the amount you should buy on each Smart DCA entry. We can use the Total invested row in the results table to calculate this. Assuming we're looking to trade on BTCUSD Decide how much USD you would spend each day to buy BTC if you were using a standard DCA strategy. Lets say that is $5 per day Enter that USD amount in the Blind buy size settings box Check the Blind Buy column in the results table. If we set the backtest date range to the last 10 years, we would expect the amount spent on blind buys over 10 years to be $18,250 given $5 each day Next we need to tweak the value of the Smart buy size parameter in setting to get it as close as we can to the Total Invested amount for Blind Buy By following this approach it means we will invest roughly the same amount into our Smart DCA strategy as we would have into a standard DCA strategy over any given time period. After you have calculated the Smart buy size, you can go ahead and set up alerts on Smart DCA buy triggers. BOT AUTOMATION In an effort to maintain the 'set and forget' stress-free benefits of a standard DCA strategy, I have set my personal Smart DCA Strategy up to be automated. The bot runs on AWS and I have a fully functional project for the bot on my GitHub account. Just reach out if you would like me to point you towards it. You can also hook this into any other 3rd party trade automation system of your choice using the pre-configured alerts within the indicator. PLANNED FUTURE DEVELOPMENTS Currently this is purely an accumulation strategy. It does not have any sell signals right now but I have ideas on how I will build upon it to incorporate an algorithm for selling. The strategy should gradually offload profits in bull markets which generates more USD which gives more buying power to rinse and repeat the same process in the next cycle only with a bigger starting capital. Watch this space! MARKETS Crypto: This strategy has been specifically built to work on the crypto markets. It has been developed, backtested and tuned against crypto markets and I personally only run it on crypto markets to accumulate more of the coins I believe in for the long term. In the section below I will provide some backtest results from some of the top crypto assets. Stocks: I've found it is generally more profitable than a standard DCA strategy on the majority of stocks, however the results proved to be a lot more impressive on crypto. This is mainly due to the volatility and cycles found in crypto markets. The strategy makes its profits from capitalising on pullbacks in price. Good stocks on the other hand tend to move up and to the right with less significant pullbacks, therefore giving this strategy less opportunity to flourish. Forex: As this is an accumulation style investment strategy, I do not recommend that you use it to trade Forex. For more info about this strategy including backtest results, please see the full description on the invite only version of this strategy named "Smart DCA Strategy"
Pine Script® 인디케이터
kevinmck100의
2
Game Theory Trading StrategyGame Theory Trading Strategy: Explanation and Working Logic This Pine Script (version 5) code implements a trading strategy named "Game Theory Trading Strategy" in TradingView. Unlike the previous indicator, this is a full-fledged strategy with automated entry/exit rules, risk management, and backtesting capabilities. It uses Game Theory principles to analyze market behavior, focusing on herd behavior, institutional flows, liquidity traps, and Nash equilibrium to generate buy (long) and sell (short) signals. Below, I'll explain the strategy's purpose, working logic, key components, and usage tips in detail. 1. General Description Purpose: The strategy identifies high-probability trading opportunities by combining Game Theory concepts (herd behavior, contrarian signals, Nash equilibrium) with technical analysis (RSI, volume, momentum). It aims to exploit market inefficiencies caused by retail herd behavior, institutional flows, and liquidity traps. The strategy is designed for automated trading with defined risk management (stop-loss/take-profit) and position sizing based on market conditions. Key Features: Herd Behavior Detection: Identifies retail panic buying/selling using RSI and volume spikes. Liquidity Traps: Detects stop-loss hunting zones where price breaks recent highs/lows but reverses. Institutional Flow Analysis: Tracks high-volume institutional activity via Accumulation/Distribution and volume spikes. Nash Equilibrium: Uses statistical price bands to assess whether the market is in equilibrium or deviated (overbought/oversold). Risk Management: Configurable stop-loss (SL) and take-profit (TP) percentages, dynamic position sizing based on Game Theory (minimax principle). Visualization: Displays Nash bands, signals, background colors, and two tables (Game Theory status and backtest results). Backtesting: Tracks performance metrics like win rate, profit factor, max drawdown, and Sharpe ratio. Strategy Settings: Initial capital: $10,000. Pyramiding: Up to 3 positions. Position size: 10% of equity (default_qty_value=10). Configurable inputs for RSI, volume, liquidity, institutional flow, Nash equilibrium, and risk management. Warning: This is a strategy, not just an indicator. It executes trades automatically in TradingView's Strategy Tester. Always backtest thoroughly and use proper risk management before live trading. 2. Working Logic (Step by Step) The strategy processes each bar (candle) to generate signals, manage positions, and update performance metrics. Here's how it works: a. Input Parameters The inputs are grouped for clarity: Herd Behavior (🐑): RSI Period (14): For overbought/oversold detection. Volume MA Period (20): To calculate average volume for spike detection. Herd Threshold (2.0): Volume multiplier for detecting herd activity. Liquidity Analysis (💧): Liquidity Lookback (50): Bars to check for recent highs/lows. Liquidity Sensitivity (1.5): Volume multiplier for trap detection. Institutional Flow (🏦): Institutional Volume Multiplier (2.5): For detecting large volume spikes. Institutional MA Period (21): For Accumulation/Distribution smoothing. Nash Equilibrium (⚖️): Nash Period (100): For calculating price mean and standard deviation. Nash Deviation (0.02): Multiplier for equilibrium bands. Risk Management (🛡️): Use Stop-Loss (true): Enables SL at 2% below/above entry price. Use Take-Profit (true): Enables TP at 5% above/below entry price. b. Herd Behavior Detection RSI (14): Checks for extreme conditions: Overbought: RSI > 70 (potential herd buying). Oversold: RSI < 30 (potential herd selling). Volume Spike: Volume > SMA(20) x 2.0 (herd_threshold). Momentum: Price change over 10 bars (close - close ) compared to its SMA(20). Herd Signals: Herd Buying: RSI > 70 + volume spike + positive momentum = Retail buying frenzy (red background). Herd Selling: RSI < 30 + volume spike + negative momentum = Retail selling panic (green background). c. Liquidity Trap Detection Recent Highs/Lows: Calculated over 50 bars (liquidity_lookback). Psychological Levels: Nearest round numbers (e.g., $100, $110) as potential stop-loss zones. Trap Conditions: Up Trap: Price breaks recent high, closes below it, with a volume spike (volume > SMA x 1.5). Down Trap: Price breaks recent low, closes above it, with a volume spike. Visualization: Traps are marked with small red/green crosses above/below bars. d. Institutional Flow Analysis Volume Check: Volume > SMA(20) x 2.5 (inst_volume_mult) = Institutional activity. Accumulation/Distribution (AD): Formula: ((close - low) - (high - close)) / (high - low) * volume, cumulated over time. Smoothed with SMA(21) (inst_ma_length). Accumulation: AD > MA + high volume = Institutions buying. Distribution: AD < MA + high volume = Institutions selling. Smart Money Index: (close - open) / (high - low) * volume, smoothed with SMA(20). Positive = Smart money buying. e. Nash Equilibrium Calculation: Price mean: SMA(100) (nash_period). Standard deviation: stdev(100). Upper Nash: Mean + StdDev x 0.02 (nash_deviation). Lower Nash: Mean - StdDev x 0.02. Conditions: Near Equilibrium: Price between upper and lower Nash bands (stable market). Above Nash: Price > upper band (overbought, sell potential). Below Nash: Price < lower band (oversold, buy potential). Visualization: Orange line (mean), red/green lines (upper/lower bands). f. Game Theory Signals The strategy generates three types of signals, combined into long/short triggers: Contrarian Signals: Buy: Herd selling + (accumulation or down trap) = Go against retail panic. Sell: Herd buying + (distribution or up trap). Momentum Signals: Buy: Below Nash + positive smart money + no herd buying. Sell: Above Nash + negative smart money + no herd selling. Nash Reversion Signals: Buy: Below Nash + rising close (close > close ) + volume > MA. Sell: Above Nash + falling close + volume > MA. Final Signals: Long Signal: Contrarian buy OR momentum buy OR Nash reversion buy. Short Signal: Contrarian sell OR momentum sell OR Nash reversion sell. g. Position Management Position Sizing (Minimax Principle): Default: 1.0 (10% of equity). In Nash equilibrium: Reduced to 0.5 (conservative). During institutional volume: Increased to 1.5 (aggressive). Entries: Long: If long_signal is true and no existing long position (strategy.position_size <= 0). Short: If short_signal is true and no existing short position (strategy.position_size >= 0). Exits: Stop-Loss: If use_sl=true, set at 2% below/above entry price. Take-Profit: If use_tp=true, set at 5% above/below entry price. Pyramiding: Up to 3 concurrent positions allowed. h. Visualization Nash Bands: Orange (mean), red (upper), green (lower). Background Colors: Herd buying: Red (90% transparency). Herd selling: Green. Institutional volume: Blue. Signals: Contrarian buy/sell: Green/red triangles below/above bars. Liquidity traps: Red/green crosses above/below bars. Tables: Game Theory Table (Top-Right): Herd Behavior: Buying frenzy, selling panic, or normal. Institutional Flow: Accumulation, distribution, or neutral. Nash Equilibrium: In equilibrium, above, or below. Liquidity Status: Trap detected or safe. Position Suggestion: Long (green), Short (red), or Wait (gray). Backtest Table (Bottom-Right): Total Trades: Number of closed trades. Win Rate: Percentage of winning trades. Net Profit/Loss: In USD, colored green/red. Profit Factor: Gross profit / gross loss. Max Drawdown: Peak-to-trough equity drop (%). Win/Loss Trades: Number of winning/losing trades. Risk/Reward Ratio: Simplified Sharpe ratio (returns / drawdown). Avg Win/Loss Ratio: Average win per trade / average loss per trade. Last Update: Current time. i. Backtesting Metrics Tracks: Total trades, winning/losing trades. Win rate (%). Net profit ($). Profit factor (gross profit / gross loss). Max drawdown (%). Simplified Sharpe ratio (returns / drawdown). Average win/loss ratio. Updates metrics on each closed trade. Displays a label on the last bar with backtest period, total trades, win rate, and net profit. j. Alerts No explicit alertconditions defined, but you can add them for long_signal and short_signal (e.g., alertcondition(long_signal, "GT Long Entry", "Long Signal Detected!")). Use TradingView's alert system with Strategy Tester outputs. 3. Usage Tips Timeframe: Best for H1-D1 timeframes. Shorter frames (M1-M15) may produce noisy signals. Settings: Risk Management: Adjust sl_percent (e.g., 1% for volatile markets) and tp_percent (e.g., 3% for scalping). Herd Threshold: Increase to 2.5 for stricter herd detection in choppy markets. Liquidity Lookback: Reduce to 20 for faster markets (e.g., crypto). Nash Period: Increase to 200 for longer-term analysis. Backtesting: Use TradingView's Strategy Tester to evaluate performance. Check win rate (>50%), profit factor (>1.5), and max drawdown (<20%) for viability. Test on different assets/timeframes to ensure robustness. Live Trading: Start with a demo account. Combine with other indicators (e.g., EMAs, support/resistance) for confirmation. Monitor liquidity traps and institutional flow for context. Risk Management: Always use SL/TP to limit losses. Adjust position_size for risk tolerance (e.g., 5% of equity for conservative trading). Avoid over-leveraging (pyramiding=3 can amplify risk). Troubleshooting: If no trades are executed, check signal conditions (e.g., lower herd_threshold or liquidity_sensitivity). Ensure sufficient historical data for Nash and liquidity calculations. If tables overlap, adjust position.top_right/bottom_right coordinates. 4. Key Differences from the Previous Indicator Indicator vs. Strategy: The previous code was an indicator (VP + Game Theory Integrated Strategy) focused on visualization and alerts. This is a strategy with automated entries/exits and backtesting. Volume Profile: Absent in this strategy, making it lighter but less focused on high-volume zones. Wick Analysis: Not included here, unlike the previous indicator's heavy reliance on wick patterns. Backtesting: This strategy includes detailed performance metrics and a backtest table, absent in the indicator. Simpler Signals: Focuses on Game Theory signals (contrarian, momentum, Nash reversion) without the "Power/Ultra Power" hierarchy. Risk Management: Explicit SL/TP and dynamic position sizing, not present in the indicator. 5. Conclusion The "Game Theory Trading Strategy" is a sophisticated system leveraging herd behavior, institutional flows, liquidity traps, and Nash equilibrium to trade market inefficiencies. It’s designed for traders who understand Game Theory principles and want automated execution with robust risk management. However, it requires thorough backtesting and parameter optimization for specific markets (e.g., forex, crypto, stocks). The backtest table and visual aids make it easy to monitor performance, but always combine with other analysis tools and proper capital management. If you need help with backtesting, adding alerts, or optimizing parameters, let me know!
Pine Script™ 전략
EfeErdost의