Multi indicator Composite Trading Strategy

Pine ScriptMachine learning Mean reversion Momentum RSI MACD SMA Bollinger Bands Stochastic CCI

Author: ChaoZhang
Complexity: Intermediate · 54 lines
Parameters: 10
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The multi-indicator composite trading strategy integrates four major indicators: moving average convergence divergence (MACD), relative strength index (RSI), commodity channel index (CCI) and stochastic relative strength index (StochRSI). It is a composite trading strategy that analyzes signals across these four indicators. By judging indicator signals across different timeframes, this strategy can more accurately identify market entry and exit points.

Strategy Logic

This strategy mainly makes judgments based on four indicators:

MACD: Calculates the difference between fast and slow moving averages to judge price momentum and trends. A buy signal is generated when the fast line crosses above the slow line.
RSI: Calculates the magnitude of price changes over a period of time. An RSI above 70 indicates overbought conditions and below 30 oversold. This strategy uses 70 and 30 as thresholds.
CCI: Measures price momentum by calculating the percentage deviation of price from its moving average. This strategy uses 100 and -100 as thresholds.
StochRSI: Combines Stochastics and RSI. A golden cross between the StochRSI %K and %D lines signals a buy, while a death cross signals a sell.

Only when all four indicators meet the criteria simultaneously will an actual buy or sell signal be generated.

Advantages

The key advantages of this multi-indicator strategy are:

Filters false signals by requiring agreement of all indicators, avoiding chasing tops or panic selling bottoms.
Captures primary trends across different dimensions by combining different indicator perspectives.
Large parameter optimization space to tune each indicator for overall optimal performance.
Weights can be adjusted based on bull or bear markets to focus on trend or mean reversion strategies.

Risks

The main risks are:

Indicators may generate concurrent false signals, triggering incorrect trades.
Prices can move violently enough for concurrent false signals across indicators.
Delayed buy signals as indicators align.
Difficult to optimize many parameters, possibly overfit.

Mitigations include parameter tuning, stop losses, and position sizing control.

Enhancement Opportunities

Enhancement opportunities:

Test combinations with more indicators like KD, Bollinger Bands to find optimal portfolio.
Optimize parameters for highest overall performance, maybe via machine learning.
Customize parameters for different stocks and sectors.
Add stop loss mechanisms in strategy code, like selling when price breaches support.
Select stocks with strong performance within sectors to improve portfolio returns.

Conclusion

This strategy integrates signals across four major indicators – MACD, RSI, CCI, and StochRSI. By setting strict entry and exit criteria based on multi-timeframe analysis, it can effectively identify market turning points. Refinements like parameter optimization, updating stock universe, and adding stops can further improve performance. Overall an effective quantitative trading strategy.

Parameters

Name	Default	What it controls
v_input_1	12	Fast Length
v_input_2	26	Slow Length
v_input_3	9	Signal Length
v_input_4	14	RSI Length
v_input_5	70	RSI Overbought Level
v_input_6	8	CCI Length
v_input_7	100	CCI Overbought Level
v_input_8	14	Stoch Length
v_input_9	3	Stoch K
v_input_10	3	Stoch D

Source Pine Script

/*backtest
start: 2023-12-01 00:00:00
end: 2023-12-31 23:59:59
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
strategy("MACD RSI CCI StochRSI Strategy", shorttitle="MRCSS", overlay=true)

// MACD göstergesi
fastLength = input(12, title="Fast Length")
slowLength = input(26, title="Slow Length")
signalLength = input(9, title="Signal Length")
[macdLine, signalLine, _] = macd(close, fastLength, slowLength, signalLength)

// RSI göstergesi
rsiLength = input(14, title="RSI Length")
rsiLevel = input(70, title="RSI Overbought Level")
rsiValue = rsi(close, rsiLength)

// CCI göstergesi
cciLength = input(8, title="CCI Length")
cciLevel = input(100, title="CCI Overbought Level")
cciValue = cci(close, cciLength)

// Stochastic Oscillator göstergesi
stochLength = input(14, title="Stoch Length")
stochK = input(3, title="Stoch K")
stochD = input(3, title="Stoch D")
stochValue = stoch(close, high, low, stochLength)
stochDValue = sma(stochValue, stochD)

// Alış ve Satış Sinyalleri
buySignal = crossover(macdLine, signalLine) and rsiValue < rsiLevel and cciValue < cciLevel and stochValue > stochDValue
sellSignal = crossunder(macdLine, signalLine) and rsiValue > (100 - rsiLevel) and cciValue > (100 - cciLevel) and stochValue < stochDValue

// Ticaret stratejisi uygula
strategy.entry("Buy", strategy.long, when = buySignal)
strategy.close("Buy", when = sellSignal)
strategy.entry("Sell", strategy.short, when = sellSignal)
strategy.close("Sell", when = buySignal)

// Göstergeleri çiz
hline(rsiLevel, "RSI Overbought", color=color.red)
hline(100 - rsiLevel, "RSI Oversold", color=color.green)
hline(cciLevel, "CCI Overbought", color=color.red)
hline(100 - cciLevel, "CCI Oversold", color=color.green)

// Grafik üzerinde sinyal okları çiz
plotshape(series=buySignal, title="Buy Signal", color=color.green, style=shape.triangleup, location=location.belowbar, size=size.small)
plotshape(series=sellSignal, title="Sell Signal", color=color.red, style=shape.triangledown, location=location.abovebar, size=size.small)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-29. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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