AI+ Scalper Strategy [BuBigMoneyMazz]Based on the AI+ Scalper Strategy
A trend-following swing strategy that uses multi-factor confirmation (trend, momentum, volatility) to capture sustained moves. Works best in trending markets and avoids choppy conditions using ADX filter.
🎯 5-Minute Chart Settings (Scalping)
pine
// RISK MANAGEMENT
ATR Multiplier SL: 1.2
ATR Multiplier TP: 2.4
// STRATEGY OPTIONS
Use HTF Filter: ON
HTF Timeframe: 15
Latching Mode: OFF
// INDICATOR SETTINGS
ADX Length: 10
ATR Length: 10
HMA Length: 14
Momentum Mode: Stochastic RSI
// STOCH RSI
Stoch RSI Length: 10
%K Smoothing: 2
%D Smoothing: 2
5-Minute Trading Style:
Quick scalps (15-45 minute holds)
Tight stops for fast markets
More frequent signals
Best during high volatility sessions (market open/close)
📈 15-Minute Chart Settings (Day Trading)
pine
// RISK MANAGEMENT
ATR Multiplier SL: 1.5
ATR Multiplier TP: 3.0
// STRATEGY OPTIONS
Use HTF Filter: ON
HTF Timeframe: 60
Latching Mode: ON
// INDICATOR SETTINGS
ADX Length: 14
ATR Length: 14
HMA Length: 21
Momentum Mode: Fisher RSI
// STOCH RSI
Stoch RSI Length: 12
%K Smoothing: 3
%D Smoothing: 3
15-Minute Trading Style:
Swing trades (1-4 hour holds)
Better risk-reward ratio
Fewer, higher quality signals
Works throughout trading day
⚡ Best Trading Times:
5-min: Market open (9:30-11:30 ET) & close (3:00-4:00 ET)
15-min: All day, but best 10:00-3:00 ET
✅ Filter for High-Probability Trades:
Only trade when ADX > 20 (strong trend)
Wait for HTF confirmation (prevents false signals)
Avoid low volume periods (lunch time)
⛔ When to Avoid Trading:
ADX < 15 (choppy market)
Major news events
First/last 15 minutes of session
Pro Tip: Start with 15-minute settings for better consistency, then move to 5-minute once you're comfortable with the strategy's behavior.
빌 윌리엄스 인디케이터
kaka 谈趋势The Exponential Moving Average (EMA) strategy is a popular technical analysis tool used in trading to smooth price data over a specific time period. The EMA gives more weight to recent prices, making it more responsive to recent price changes compared to the Simple Moving Average (SMA).
Nifty 50 Scalping - Bullish Buy & Bearish Sell (5 Target / 2 SL)Nifty 50 Scalping - Bullish Buy & Bearish Sell (5 Target / 2 SL)
TheWave + Supertrend Hybrid w/ Signals• Green triangle below bar → Long entry signal
• Red triangle above bar → Short entry signal
• Small lime cross above/below → Take-profit hit
• Small maroon cross above/below → Stop-loss hit
• SMA5 and Supertrend lines for trend context
This version makes all entries and exits visually obvious while keeping the hybrid TheWave + Supertrend logic intact.
Trend River Pullback (Avramis-style) v1//@version=5
strategy("Trend River Pullback (Avramis-style) v1",
overlay=true, initial_capital=10000, commission_type=strategy.commission.percent, commission_value=0.02,
pyramiding=0, calc_on_order_fills=true, calc_on_every_tick=true, margin_long=1, margin_short=1)
// ===== Inputs
// EMA "река"
emaFastLen = input.int(8, "EMA1 (быстрая)")
ema2Len = input.int(13, "EMA2")
emaMidLen = input.int(21, "EMA3 (средняя)")
ema4Len = input.int(34, "EMA4")
emaSlowLen = input.int(55, "EMA5 (медленная)")
// Откат и импульс
rsiLen = input.int(14, "RSI длина")
rsiOB = input.int(60, "RSI порог тренда (лонг)")
rsiOS = input.int(40, "RSI порог тренда (шорт)")
pullbackPct = input.float(40.0, "Глубина отката в % ширины реки", minval=0, maxval=100)
// Риск-менеджмент
riskPct = input.float(1.0, "Риск на сделку, % от капитала", step=0.1, minval=0.1)
atrLen = input.int(14, "ATR длина (стоп/трейлинг)")
atrMultSL = input.float(2.0, "ATR множитель для стопа", step=0.1)
tpRR = input.float(2.0, "Тейк-профит R-множитель", step=0.1)
// Трейлинг-стоп
useTrail = input.bool(true, "Включить трейлинг-стоп (Chandelier)")
trailMult = input.float(3.0, "ATR множитель трейлинга", step=0.1)
// Торговые часы (по времени биржи TradingView символа)
useSession = input.bool(false, "Ограничить торговые часы")
sessInput = input.session("0900-1800", "Сессия (локальная для биржи)")
// ===== Calculations
ema1 = ta.ema(close, emaFastLen)
ema2 = ta.ema(close, ema2Len)
ema3 = ta.ema(close, emaMidLen)
ema4 = ta.ema(close, ema4Len)
ema5 = ta.ema(close, emaSlowLen)
// "Река": верх/низ как конверт по средним
riverTop = math.max(math.max(ema1, ema2), math.max(ema3, math.max(ema4, ema5)))
riverBot = math.min(math.min(ema1, ema2), math.min(ema3, math.min(ema4, ema5)))
riverMid = (riverTop + riverBot) / 2.0
riverWidth = riverTop - riverBot
// Трендовые условия: выстроенность EMAs
bullAligned = ema1 > ema2 and ema2 > ema3 and ema3 > ema4 and ema4 > ema5
bearAligned = ema1 < ema2 and ema2 < ema3 and ema3 < ema4 and ema4 < ema5
// Импульс
rsi = ta.rsi(close, rsiLen)
// Откат внутрь "реки"
pullbackLevelBull = riverTop - riverWidth * (pullbackPct/100.0) // чем больше %, тем глубже внутрь
pullbackLevelBear = riverBot + riverWidth * (pullbackPct/100.0)
pullbackOkBull = bullAligned and rsi >= rsiOB and low <= pullbackLevelBull
pullbackOkBear = bearAligned and rsi <= rsiOS and high >= pullbackLevelBear
// Триггер входа: возврат в импульс (пересечение быстрой EMA)
longTrig = pullbackOkBull and ta.crossover(close, ema1)
shortTrig = pullbackOkBear and ta.crossunder(close, ema1)
// Сессия
inSession = useSession ? time(timeframe.period, sessInput) : true
// ATR для стопов
atr = ta.atr(atrLen)
// ===== Position sizing по риску
// Расчет размера позиции: риск% от капитала / (стоп в деньгах)
capital = strategy.equity
riskMoney = capital * (riskPct/100.0)
// Предварительные уровни стопов
longSL = close - atrMultSL * atr
shortSL = close + atrMultSL * atr
// Цена тика и размер — приблизительно через syminfo.pointvalue (может отличаться на разных рынках)
tickValue = syminfo.pointvalue
// Избежать деления на 0
slDistLong = math.max(close - longSL, syminfo.mintick)
slDistShort = math.max(shortSL - close, syminfo.mintick)
// Кол-во контрактов/лотов
qtyLong = riskMoney / (slDistLong * tickValue)
qtyShort = riskMoney / (slDistShort * tickValue)
// Ограничение: не меньше 0
qtyLong := math.max(qtyLong, 0)
qtyShort := math.max(qtyShort, 0)
// ===== Entries
if inSession and longTrig and strategy.position_size <= 0
strategy.entry("Long", strategy.long, qty=qtyLong)
if inSession and shortTrig and strategy.position_size >= 0
strategy.entry("Short", strategy.short, qty=qtyShort)
// ===== Exits: фиксированный TP по R и стоп
// Храним цену входа
var float entryPrice = na
if strategy.position_size != 0 and na(entryPrice)
entryPrice := strategy.position_avg_price
if strategy.position_size == 0
entryPrice := na
// Цели
longTP = na(entryPrice) ? na : entryPrice + tpRR * (entryPrice - longSL)
shortTP = na(entryPrice) ? na : entryPrice - tpRR * (shortSL - entryPrice)
// Трейлинг: Chandelier
trailLong = close - trailMult * atr
trailShort = close + trailMult * atr
// Итоговые уровни выхода
useTrailLong = useTrail and strategy.position_size > 0
useTrailShort = useTrail and strategy.position_size < 0
// Для лонга
if strategy.position_size > 0
stopL = math.max(longSL, na) // базовый стоп
tStop = useTrailLong ? trailLong : longSL
// Выход по стопу/трейлу и ТП
strategy.exit("L-Exit", from_entry="Long", stop=tStop, limit=longTP)
// Для шорта
if strategy.position_size < 0
stopS = math.min(shortSL, na)
tStopS = useTrailShort ? trailShort : shortSL
strategy.exit("S-Exit", from_entry="Short", stop=tStopS, limit=shortTP)
// ===== Visuals
plot(ema1, "EMA1", display=display.all, linewidth=1)
plot(ema2, "EMA2", display=display.all, linewidth=1)
plot(ema3, "EMA3", display=display.all, linewidth=2)
plot(ema4, "EMA4", display=display.all, linewidth=1)
plot(ema5, "EMA5", display=display.all, linewidth=1)
plot(riverTop, "River Top", style=plot.style_linebr, linewidth=1)
plot(riverBot, "River Bot", style=plot.style_linebr, linewidth=1)
fill(plot1=plot(riverTop, display=display.none), plot2=plot(riverBot, display=display.none), title="River Fill", transp=80)
plot(longTP, "Long TP", style=plot.style_linebr)
plot(shortTP, "Short TP", style=plot.style_linebr)
plot(useTrailLong ? trailLong : na, "Trail Long", style=plot.style_linebr)
plot(useTrailShort ? trailShort : na, "Trail Short", style=plot.style_linebr)
// Маркеры сигналов
plotshape(longTrig, title="Long Trigger", style=shape.triangleup, location=location.belowbar, size=size.tiny, text="L")
plotshape(shortTrig, title="Short Trigger", style=shape.triangledown, location=location.abovebar, size=size.tiny, text="S")
// ===== Alerts
alertcondition(longTrig, title="Long Signal", message="Long signal: trend aligned + pullback + momentum")
alertcondition(shortTrig, title="Short Signal", message="Short signal: trend aligned + pullback + momentum")
Gamma Blast StrategyGamma Blast Strategy used for quick 2-5 ticks on Buys, but on a sideways market can get up to 15-20 ticks.
OPTIMAL super trend tripple confirm for leverage. Ai implemented for higher r:r still a work in progresss
🚀⚠️ Aggressive + Confirmed Long Strategy (v2)//@version=5
strategy("🚀⚠️ Aggressive + Confirmed Long Strategy (v2)",
overlay=true,
pyramiding=0,
initial_capital=10000,
default_qty_type=strategy.percent_of_equity,
default_qty_value=10, // % of equity per trade
commission_type=strategy.commission.percent,
commission_value=0.05)
// ========= Inputs =========
lenRSI = input.int(14, "RSI Length")
lenSMA1 = input.int(20, "SMA 20")
lenSMA2 = input.int(50, "SMA 50")
lenBB = input.int(20, "Bollinger Length")
multBB = input.float(2, "Bollinger Multiplier", step=0.1)
volLen = input.int(20, "Volume MA Length")
smaBuffP = input.float(1.0, "Margin above SMA50 (%)", step=0.1)
confirmOnClose = input.bool(true, "Confirm signals only after candle close")
useEarly = input.bool(true, "Allow Early entries")
// Risk
atrLen = input.int(14, "ATR Length", minval=1)
slATR = input.float(2.0, "Stop = ATR *", step=0.1)
tpRR = input.float(2.0, "Take-Profit RR (TP = SL * RR)", step=0.1)
useTrail = input.bool(false, "Use Trailing Stop instead of fixed SL/TP")
trailATR = input.float(2.5, "Trailing Stop = ATR *", step=0.1)
moveToBE = input.bool(true, "Move SL to breakeven at 1R TP")
// ========= Indicators =========
// MAs
sma20 = ta.sma(close, lenSMA1)
sma50 = ta.sma(close, lenSMA2)
// RSI
rsi = ta.rsi(close, lenRSI)
rsiEarly = rsi > 45 and rsi < 55
rsiStrong = rsi > 55
// MACD
= ta.macd(close, 12, 26, 9)
macdCross = ta.crossover(macdLine, signalLine)
macdEarly = macdCross and macdLine < 0
macdStrong = macdCross and macdLine > 0
// Bollinger
= ta.bb(close, lenBB, multBB)
bollBreakout = close > bbUpper
// Candle & Volume
bullishCandle = close > open
volCondition = volume > ta.sma(volume, volLen)
// Price vs MAs
smaCondition = close > sma20 and close > sma50 and close > sma50 * (1 + smaBuffP/100.0)
// Confirm-on-close helper
useSignal(cond) =>
confirmOnClose ? (cond and barstate.isconfirmed) : cond
// Entries
confirmedEntry = useSignal(rsiStrong and macdStrong and bollBreakout and bullishCandle and volCondition and smaCondition)
earlyEntry = useSignal(rsiEarly and macdEarly and close > sma20 and bullishCandle) and not confirmedEntry
longSignal = confirmedEntry or (useEarly and earlyEntry)
// ========= Risk Mgmt =========
atr = ta.atr(atrLen)
slPrice = close - atr * slATR
tpPrice = close + (close - slPrice) * tpRR
trailPts = atr * trailATR
// ========= Orders =========
if strategy.position_size == 0 and longSignal
strategy.entry("Long", strategy.long)
if strategy.position_size > 0
if useTrail
// Trailing Stop
strategy.exit("Exit", "Long", trail_points=trailPts, trail_offset=trailPts)
else
// Normal SL/TP
strategy.exit("Exit", "Long", stop=slPrice, limit=tpPrice)
// Move SL to breakeven when TP1 hit
if moveToBE and high >= tpPrice
strategy.exit("BE", "Long", stop=strategy.position_avg_price)
// ========= Plots =========
plot(sma20, title="SMA 20", color=color.orange, linewidth=2)
plot(sma50, title="SMA 50", color=color.new(color.blue, 0), linewidth=2)
plot(bbUpper, title="BB Upper", color=color.new(color.fuchsia, 0))
plot(bbBasis, title="BB Basis", color=color.new(color.gray, 50))
plot(bbLower, title="BB Lower", color=color.new(color.fuchsia, 0))
plotshape(confirmedEntry, title="🚀 Confirmed", location=location.belowbar,
color=color.green, style=shape.labelup, text="🚀", size=size.tiny)
plotshape(earlyEntry, title="⚠️ Early", location=location.belowbar,
color=color.orange, style=shape.labelup, text="⚠️", size=size.tiny)
// ========= Alerts =========
alertcondition(confirmedEntry, title="🚀 Confirmed Entry", message="🚀 {{ticker}} confirmed entry on {{interval}}")
alertcondition(earlyEntry, title="⚠️ Early Entry", message="⚠️ {{ticker}} early entry on {{interval}}")
Estrategy EURUSD M3 Scalping Estrategia para operar el EURUSD en temp de 3 min, indica sl y tp 6 pips sl y 10 pips tp
Twin Range Filter StrategyClarity Over Confusion: See price action through a全新的 lens. Watch as erratic, choppy movements are smoothed into a clear, actionable trajectory. The path of least resistance becomes obvious.
Confidence Over Hesitation: Receive high-probability entry and exit signals with a proven logic that waits for the market to commit before you do. No more second-guessing.
Discipline Over Emotion: Our algorithm enforces a systematic approach, helping you avoid emotional FOMO chasing and panic selling. Stick to the plan and execute with precision.
What Can You Expect?
Dynamic Adaptability: Unlike static indicators, continuously adapts to volatility. It widens its filter in turbulent markets to avoid whipsaws and tightens it in trending markets to capture more of the move.
The Power of Two: By synthesizing data from two distinct market perspectives, it confirms strength and filters out weakness, providing a confluence that standalone indicators simply cannot match.
Clean, Unambiguous Signals: We’ve eliminated the clutter. The software provides clear visual alerts (Green Arrows for Long, Red Arrows for Short) right on your chart, telling you exactly when the equilibrium has shifted.
Who is this for?
Swing Traders looking to capture the heart of a trend and avoid false breakouts.
Day Traders needing a reliable filter to navigate volatile intraday action.
Systematic Traders seeking a robust logic layer to add to their automated strategy.
Anyone overwhelmed by indicator overload and craving a single, trusted source of truth on their chart
AYUSH ALGO TRAGING STRATEGY TEST VERSION 1)Very good strategy , it uses two moving avg crossovers and also rsi and atr for confirmation, this strategy is fully automated
M1 Countertrend Scalping (Best-effort)M1 Countertrend Scalping (Best-effort)
M1 Countertrend Scalping (Best-effort)
DCA Strategy on Steroids for CryptoThis strategy getting only in Long position for Crypto
Using Fast and Slow moving Averages and Stochastic RSI to get in Long position
Fast and Slow moving Averages - cross-under - I Prefer - or opposite for Bull Market
Stochastic RSI cross-over - 5 and Trend Determined by the Fast moving Average
There is no Stop loss is not for one with small tolerance to getting under
Fast and Slow moving Averages and Stochastic RSI Parameters can be adjust
The bot Use Safe Trades and Price Deviation Determined from the User
Max Safe Trades = 10
Take profit Parameters can be adjust in %
Pepe-USDC is just a example What the bot Can do
Nova Futures PRO (SAFE v6) — HTF + Choppiness + CooldownNova Futures PRO (SAFE v6) — HTF + Choppiness + Cooldown
Martin Strategy - No Loss Exit v3Martin Strategy - No Loss Exit v3Martin Strategy - No Loss Exit v3Martin Strategy - No Loss Exit v3
Manadi Buy/Sell Strategy EMA + MACD + RSI + AlertsIt is a strategy / indicator of buy and sell special crypto for 15 min to 1 h time frame.
used with RSI, Macd, and Ema cros 9/21
CE XAU/USDT Strategy📌 Auto-Trading Strategy Using CE on XAU/USDT (5M)
Indicator: CE
Parameters:
• ATR Period: 1
• ATR Multiplier: 1.85
Timeframe: 5 minutes
Instrument: Gold (XAU/USD)
🔁 Logic:
• Buy signal → Close short, open long
• Sell signal → Close long, open short
⚙️ Automation:
1. CE indicator on TradingView generates signals
2. Signals are sent via webhook to a Python bot
3. The bot opens/closes trades in MT5 accordingly
✅ Advantages:
• Full automation
• Operates 24/7 without manual intervention
⚠️ Important:
• Always test on a demo account
• Manage risk and position size properly
📌 Стратегия автоторговли по CE на XAU/USDT (5М)
Индикатор: CE
Параметры:
• ATR Period: 1
• ATR Множитель: 1.85
Таймфрейм: 5 минут
Инструмент: Золото (XAU/USD)
🔁 Логика:
• Buy сигнал → закрыть шорт, открыть лонг
• Sell сигнал → закрыть лонг, открыть шорт
⚙️ Автоматизация:
1. CE в TradingView генерирует сигналы
2. Webhook отправляет их в Python-бот
3. Бот открывает/закрывает сделки в MT5
✅ Плюсы:
• Полная автоматизация
• Работа 24/7 без вмешательства
⚠️ Важно:
• Тестируй на демо
• Управляй рисками и лотами
BB + RSI Strategy Optimized✅ Pine Script Version 5
✅ Complete Strategy: Long + Short
✅ Automatic Entry and Exit
✅ Visual Signals: Buy/Sell, Short/Cover
✅ Trailing Take Profit
✅ Progressive
30M Scalping Strategy with Debug LogsWhat’s changed
Spot‑only: all short logic removed—only long entries and exits are generated.
Logging: uses log.info() to send entry/exit details (timestamp, price, ATR, RSI) to the Pine Logs console.
Clean & concise: core scalp logic (EMAs, RSI, MACD, volume, ATR SL/TP) remains intact.
Supertrend Strategy (5m)📊 Strategy: Buy/Sell Based on EMA Crossover (5-Minute Timeframe)
📊 Стратегия: Buy/Sell по пересечению EMA (5 минут)
KST Strategy [Skyrexio]Overview
KST Strategy leverages Know Sure Thing (KST) indicator in conjunction with the Williams Alligator and Moving average to obtain the high probability setups. KST is used for for having the high probability to enter in the direction of a current trend when momentum is rising, Alligator is used as a short term trend filter, while Moving average approximates the long term trend and allows trades only in its direction. Also strategy has the additional optional filter on Choppiness Index which does not allow trades if market is choppy, above the user-specified threshold. Strategy has the user specified take profit and stop-loss numbers, but multiplied by Average True Range (ATR) value on the moment when trade is open. The strategy opens only long trades.
Unique Features
ATR based stop-loss and take profit. Instead of fixed take profit and stop-loss percentage strategy utilizes user chosen numbers multiplied by ATR for its calculation.
Configurable Trading Periods. Users can tailor the strategy to specific market windows, adapting to different market conditions.
Optional Choppiness Index filter. Strategy allows to choose if it will use the filter trades with Choppiness Index and set up its threshold.
Methodology
The strategy opens long trade when the following price met the conditions:
Close price is above the Alligator's jaw line
Close price is above the filtering Moving average
KST line of Know Sure Thing indicator shall cross over its signal line (details in justification of methodology)
If the Choppiness Index filter is enabled its value shall be less than user defined threshold
When the long trade is executed algorithm defines the stop-loss level as the low minus user defined number, multiplied by ATR at the trade open candle. Also it defines take profit with close price plus user defined number, multiplied by ATR at the trade open candle. While trade is in progress, if high price on any candle above the calculated take profit level or low price is below the calculated stop loss level, trade is closed.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.5, number of ATRs to calculate stop-loss level)
ATR Take Profit (by default = 3.5, number of ATRs to calculate take profit level)
Filter MA Type (by default = Least Squares MA, type of moving average which is used for filter MA)
Filter MA Length (by default = 200, length for filter MA calculation)
Enable Choppiness Index Filter (by default = true, setting to choose the optional filtering using Choppiness index)
Choppiness Index Threshold (by default = 50, Choppiness Index threshold, its value shall be below it to allow trades execution)
Choppiness Index Length (by default = 14, length used in Choppiness index calculation)
KST ROC Length #1 (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
KST ROC Length #2 (by default = 15, value used in KST indicator calculation, more information in Justification of Methodology)
KST ROC Length #3 (by default = 20, value used in KST indicator calculation, more information in Justification of Methodology)
KST ROC Length #4 (by default = 30, value used in KST indicator calculation, more information in Justification of Methodology)
KST SMA Length #1 (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
KST SMA Length #2 (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
KST SMA Length #3 (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
KST SMA Length #4 (by default = 15, value used in KST indicator calculation, more information in Justification of Methodology)
KST Signal Line Length (by default = 10, value used in KST indicator calculation, more information in Justification of Methodology)
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Before understanding why this particular combination of indicator has been chosen let's briefly explain what is KST, Williams Alligator, Moving Average, ATR and Choppiness Index.
The KST (Know Sure Thing) is a momentum oscillator developed by Martin Pring. It combines multiple Rate of Change (ROC) values, smoothed over different timeframes, to identify trend direction and momentum strength. First of all, what is ROC? ROC (Rate of Change) is a momentum indicator that measures the percentage change in price between the current price and the price a set number of periods ago.
ROC = 100 * (Current Price - Price N Periods Ago) / Price N Periods Ago
In our case N is the KST ROC Length inputs from settings, here we will calculate 4 different ROCs to obtain KST value:
KST = ROC1_smooth × 1 + ROC2_smooth × 2 + ROC3_smooth × 3 + ROC4_smooth × 4
ROC1 = ROC(close, KST ROC Length #1), smoothed by KST SMA Length #1,
ROC2 = ROC(close, KST ROC Length #2), smoothed by KST SMA Length #2,
ROC3 = ROC(close, KST ROC Length #3), smoothed by KST SMA Length #3,
ROC4 = ROC(close, KST ROC Length #4), smoothed by KST SMA Length #4
Also for this indicator the signal line is calculated:
Signal = SMA(KST, KST Signal Line Length)
When the KST line rises, it indicates increasing momentum and suggests that an upward trend may be developing. Conversely, when the KST line declines, it reflects weakening momentum and a potential downward trend. A crossover of the KST line above its signal line is considered a buy signal, while a crossover below the signal line is viewed as a sell signal. If the KST stays above zero, it indicates overall bullish momentum; if it remains below zero, it points to bearish momentum. The KST indicator smooths momentum across multiple timeframes, helping to reduce noise and provide clearer signals for medium- to long-term trends.
Next, let’s discuss the short-term trend filter, which combines the Williams Alligator and Williams Fractals. Williams Alligator
Developed by Bill Williams, the Alligator is a technical indicator that identifies trends and potential market reversals. It consists of three smoothed moving averages:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When the lines diverge and align in order, the "Alligator" is "awake," signaling a strong trend. When the lines overlap or intertwine, the "Alligator" is "asleep," indicating a range-bound or sideways market. This indicator helps traders determine when to enter or avoid trades.
The next indicator is Moving Average. It has a lot of different types which can be chosen to filter trades and the Least Squares MA is used by default settings. Let's briefly explain what is it.
The Least Squares Moving Average (LSMA) — also known as Linear Regression Moving Average — is a trend-following indicator that uses the least squares method to fit a straight line to the price data over a given period, then plots the value of that line at the most recent point. It draws the best-fitting straight line through the past N prices (using linear regression), and then takes the endpoint of that line as the value of the moving average for that bar. The LSMA aims to reduce lag and highlight the current trend more accurately than traditional moving averages like SMA or EMA.
Key Features:
It reacts faster to price changes than most moving averages.
It is smoother and less noisy than short-term EMAs.
It can be used to identify trend direction, momentum, and potential reversal points.
ATR (Average True Range) is a volatility indicator that measures how much an asset typically moves during a given period. It was introduced by J. Welles Wilder and is widely used to assess market volatility, not direction.
To calculate it first of all we need to get True Range (TR), this is the greatest value among:
High - Low
abs(High - Previous Close)
abs(Low - Previous Close)
ATR = MA(TR, n) , where n is number of periods for moving average, in our case equals 14.
ATR shows how much an asset moves on average per candle/bar. A higher ATR means more volatility; a lower ATR means a calmer market.
The Choppiness Index is a technical indicator that quantifies whether the market is trending or choppy (sideways). It doesn't indicate trend direction — only the strength or weakness of a trend. Higher Choppiness Index usually approximates the sideways market, while its low value tells us that there is a high probability of a trend.
Choppiness Index = 100 × log10(ΣATR(n) / (MaxHigh(n) - MinLow(n))) / log10(n)
where:
ΣATR(n) = sum of the Average True Range over n periods
MaxHigh(n) = highest high over n periods
MinLow(n) = lowest low over n periods
log10 = base-10 logarithm
Now let's understand how these indicators work in conjunction and why they were chosen for this strategy. KST indicator approximates current momentum, when it is rising and KST line crosses over the signal line there is high probability that short term trend is reversing to the upside and strategy allows to take part in this potential move. Alligator's jaw (blue) line is used as an approximation of a short term trend, taking trades only above it we want to avoid trading against trend to increase probability that long trade is going to be winning.
Almost the same for Moving Average, but it approximates the long term trend, this is just the additional filter. If we trade in the direction of the long term trend we increase probability that higher risk to reward trade will hit the take profit. Choppiness index is the optional filter, but if it turned on it is used for approximating if now market is in sideways or in trend. On the range bounded market the potential moves are restricted. We want to decrease probability opening trades in such condition avoiding trades if this index is above threshold value.
When trade is open script sets the stop loss and take profit targets. ATR approximates the current volatility, so we can make a decision when to exit a trade based on current market condition, it can increase the probability that strategy will avoid the excessive stop loss hits, but anyway user can setup how many ATRs to use as a stop loss and take profit target. As was said in the Methodology stop loss level is obtained by subtracting number of ATRs from trade opening candle low, while take profit by adding to this candle's close.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2025.05.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 60%
Maximum Single Position Loss: -5.53%
Maximum Single Profit: +8.35%
Net Profit: +5175.20 USDT (+51.75%)
Total Trades: 120 (56.67% win rate)
Profit Factor: 1.747
Maximum Accumulated Loss: 1039.89 USDT (-9.1%)
Average Profit per Trade: 43.13 USDT (+0.6%)
Average Trade Duration: 27 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 1h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrexio commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation.
