Supply Demand Momentum Reversal Trading Strategy

Pine ScriptMean reversion Momentum Machine learning RSI MACD EMA ATR

Author: ChaoZhang
Complexity: Intermediate · 79 lines
Parameters: 9
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy combines momentum indicators and moving averages to identify market trends and reversal points for trading when the trend changes direction. It belongs to trend following and countertrend trading strategies. The main components include supply and demand zones, EMA, various HH, LL, LH, HL long and short zones, ATR trailing stop loss etc.

Strategy Logic

1. Supply and Demand Identification

Distinguish supply and demand relationship based on high and low range of Kline. Red areas indicate supply exceeds demand supply zones. Green areas indicate demand exceeds supply demand zones.

2. EMA Trend Judgement

Plot 200 period EMA and determine uptrend and downtrend by comparing price with EMA. Price above EMA is considered as uptrend, while price below EMA as downtrend.

3. Long and Short Zone Marking

Determine reversal zones based on recent 2 candle's high and low points:

HH Zone (Higher High Zone) - Consecutive 2 candle highs make higher high
LL Zone (Lower Low Zone) - Consecutive 2 candle lows make lower low
LH Zone (Lower High Zone) - Recent higher high reversing into lower high
HL Zone (Higher Low Zone) - Recent lower low reversing into higher low

4. ATR Trailing Stop Loss

Calculate 14 period ATR value which will be multiplied by a factor of 2 to derive the stop loss level.

5. Entry and Stop Loss Exit

Monitor price relationship with previous candle's high/low points. Long signal triggers when price breaks above previous high. Short signal triggers when price breaks below previous low. Delay entry signal confirmation until the 3rd candle to avoid false signals. Exit with stop loss when price pulls back beyond the ATR trailing stop loss level.

Advantage Analysis

Utilize multiple indicators to identify trend and key reversal areas to improve profitability rate.
ATR stop loss can effectively limit per trade loss risk.
Delayed entry signal confirmation minimizes false trading.

Risk Analysis

Rely solely on technical indicators without considering fundamental information may lead to trading failures from missing key data.
ATR stop loss may get run-over during huge volatility resulting in losses.
Frequent EMA reversal signals during ranging markets can lead to over-trading.

Risk Solutions:

Complement with economic data and policy judgements.
Allow wider buffer for ATR multiplier coefficient.
Adjust ATR period parameter to avoid sensitivity during ranges.

Enhancement Opportunities

Complement with technical indicators like MACD, RSI etc to improve timing.
Backtest different period and multiplier parameter combinations for optimization.
Consider adding re-breakout filter to avoid signal whipsaws.
Employ machine learning etc to dynamically optimize parameters.

Conclusion

This strategy combines supply/demand analysis, trend determination, reversal identification and risk management modules effectively to spot market reversal opportunities at key areas. It is a robust system for trend following and countertrend setups. Continuous testing, optimization and human experience judgements are crucial for long term steady profits.

Parameters

Name	Default	What it controls
v_input_1	true	(?Signals)Show Buy Signals
v_input_2	true	Show Sell Signals
v_input_3	true	(?Zones)Show HL Zone
v_input_4	true	Show LH Zone
v_input_5	true	Show HH Zone
v_input_6	true	Show LL Zone
v_input_7	200	(?EMA Settings)EMA Length
v_input_8	14	(?Trailing Stop)ATR Length
v_input_9	2	ATR Multiplier

Source Pine Script

/*backtest
start: 2023-12-01 00:00:00
end: 2023-12-20 23:59:59
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("Supply and Demand Zones with EMA and Trailing Stop", shorttitle="SD Zones", overlay=true)

showBuySignals = input(true, title="Show Buy Signals", group="Signals")
showSellSignals = input(true, title="Show Sell Signals", group="Signals")
showHLZone = input(true, title="Show HL Zone", group="Zones")
showLHZone = input(true, title="Show LH Zone", group="Zones")
showHHZone = input(true, title="Show HH Zone", group="Zones")
showLLZone = input(true, title="Show LL Zone", group="Zones")

emaLength = input(200, title="EMA Length", group="EMA Settings")
atrLength = input(14, title="ATR Length", group="Trailing Stop")
atrMultiplier = input(2, title="ATR Multiplier", group="Trailing Stop")

// Function to identify supply and demand zones
getZones(src, len, mult) =>
    base = request.security(syminfo.tickerid, "D", close)
    upper = request.security(syminfo.tickerid, "D", high)
    lower = request.security(syminfo.tickerid, "D", low)
    multiplier = request.security(syminfo.tickerid, "D", mult)
    zonetype = base + multiplier * len
    zone = src >= zonetype
    [zone, upper, lower]

// Identify supply and demand zones
[supplyZone, _, _] = getZones(close, high[1] - low[1], 1)
[demandZone, _, _] = getZones(close, high[1] - low[1], -1)

// Plot supply and demand zones
bgcolor(supplyZone ? color.new(color.red, 80) : na)
bgcolor(demandZone ? color.new(color.green, 80) : na)

// EMA with Linear Weighted method
ema = ta.ema(close, emaLength)

// Color code EMA based on its relation to candles
emaColor = close > ema ? color.new(color.green, 0) : close < ema ? color.new(color.red, 0) : color.new(color.yellow, 0)

// Plot EMA
plot(ema, color=emaColor, title="EMA")

// Entry Signal Conditions after the third candle
longCondition = ta.crossover(close, high[1]) and (bar_index >= 2)
shortCondition = ta.crossunder(close, low[1]) and (bar_index >= 2)

// Trailing Stop using ATR
atrValue = ta.atr(atrLength)
trailStop = close - atrMultiplier * atrValue

// Strategy Entry and Exit
if (longCondition)
    strategy.entry("Buy", strategy.long)
    strategy.exit("TrailStop", from_entry="Buy", loss=trailStop)

if (shortCondition)
    strategy.entry("Sell", strategy.short)
    strategy.exit("TrailStop", from_entry="Sell", loss=trailStop)

// Plot Entry Signals
plotshape(series=showBuySignals ? longCondition : na, title="Buy Signal", color=color.new(color.green, 0), style=shape.triangleup, location=location.belowbar)
plotshape(series=showSellSignals ? shortCondition : na, title="Sell Signal", color=color.new(color.red, 0), style=shape.triangledown, location=location.abovebar)

// Plot Trailing Stop
plot(trailStop, color=color.new(color.red, 0), title="Trailing Stop")

// Plot HH, LL, LH, and HL zones
plotshape(series=showHHZone and ta.highest(high, 2)[1] and ta.highest(high, 2)[2] ? 1 : na, title="HH Zone", color=color.new(color.blue, 80), style=shape.triangleup, location=location.abovebar)
plotshape(series=showLLZone and ta.lowest(low, 2)[1] and ta.lowest(low, 2)[2] ? 1 : na, title="LL Zone", color=color.new(color.blue, 80), style=shape.triangledown, location=location.belowbar)
plotshape(series=showLHZone and ta.highest(high, 2)[1] and ta.lowest(low, 2)[2] ? 1 : na, title="LH Zone", color=color.new(color.orange, 80), style=shape.triangleup, location=location.abovebar)
plotshape(series=showHLZone and ta.lowest(low, 2)[1] and ta.highest(high, 2)[2] ? 1 : na, title="HL Zone", color=color.new(color.orange, 80), style=shape.triangledown, location=location.belowbar)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-22. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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