Quantitative Trading Price Breakthrough Strategy

Pine ScriptBreakout Trend following Volatility MACD EMA SMA ATR Stochastic Keltner

Author: ChaoZhang
Complexity: Intermediate · 65 lines
Parameters: 15
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This is a short-term quantitative trading strategy based on Simple Moving Average (SMA), Exponential Moving Average (EMA), Keltner Channels, MACD indicator and Stochastic oscillator. It uses the price breakthrough of SMA and EMA, combined with long and short signals from Keltner Channels, MACD and Stochastic to automate trading entries and exits.

Strategy Principle

The strategy uses 25-period SMA, 200-period EMA to build dual moving average lines. When price breaks through the dual moving averages upwards, a buy signal is generated. When price breaks through the dual moving averages downwards, a sell signal is generated.

At the same time, this strategy uses 10-period Keltner Channels. The breakthrough of channel upper and lower bands also serves as assistant signals. The MACD indicator generates trading signals with its fast line, slow line and histogram. The Stochastic oscillator also forms long and short signals with the golden cross and dead cross of its %K line and %D line.

Specifically, when close price is above both SMA and EMA, and within the Keltner Channels, MACD histogram is negative and Stochastic %K is below 50, a long entry signal is triggered. When close price is below both SMA and EMA, and within the Keltner Channels, MACD histogram is positive and Stochastic %K is above 50, a short entry signal is triggered.

Strategy Advantages

Using dual moving average combined with channel indicator can effectively filter false breakouts.
Integrating signals from multiple technical indicators can improve reliability.
Clear long/short rules facilitate programmatic execution efficiency.
Suitable for high-frequency quantitative trading strategies.

Strategy Risks and Optimization

As a short-term trading strategy, it has high trading frequency risks.
No stop loss mechanism exists, leading to large loss risks.
Consider adding volatility indicators to optimize entry and stop loss conditions.
Different parameter periods can be tested to find optimal combinations.

Conclusion

This strategy integrates four commonly used technical indicators - moving averages, channel, MACD and Stochastic. It determines long/short based on price breakthrough, a typical short-term quantitative trading strategy. Compared to single indicator strategies, its multiple indicator combination improves signal accuracy and is worth further testing and optimization.

Parameters

Name	Default	What it controls
v_input_1_low	0	Source: low \| high \| close \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_int_1	25	(?Moving Averages)Length SMA
v_input_int_2	200	Length EMA
v_input_int_3	10	(?Keltner)Length Keltner Channel
v_input_2	2	Multiplier
v_input_string_1	0	Bands Style: Average True Range \| True Range \| Range
v_input_3	14	ATR Length
v_input_int_4	10	(?Stochastic)%K Length
v_input_int_5	true	%K Smoothing
v_input_int_6	true	%D Smoothing
v_input_4	4	(?MACD Fast)Fast Length MACD
v_input_5	34	Slow Length MACD
v_input_int_7	5	Signal Smoothing MACD
v_input_string_2	0	Oscillator MA Type MACD: EMA \| SMA
v_input_string_3	0	Signal Line MA Type MACD: EMA \| SMA

Source Pine Script

/*backtest
start: 2022-12-15 00:00:00
end: 2023-12-21 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © exlux99

//@version=5
strategy(title="Scalping Trading System Crypto and Stocks", overlay=true)
src = input(low, title="Source")

//sma and ema
len = input.int(25, minval=1, title="Length SMA" , group="Moving Averages")
len2 = input.int(200, minval=1, title="Length EMA", group="Moving Averages")

out = ta.sma(src, len)
out2 = ta.ema(src, len2)


//keltner
lengthk = input.int(10, minval=1, title="Length Keltner Channel",group="Keltner")
mult = input(2.0, "Multiplier",group="Keltner")
BandsStyle = input.string("Average True Range", options = ["Average True Range", "True Range", "Range"], title="Bands Style",group="Keltner")
atrlength = input(14, "ATR Length",group="Keltner")

ma = ta.sma(src, lengthk)
rangema = BandsStyle == "True Range" ? ta.tr(true) : BandsStyle == "Average True Range" ? ta.atr(atrlength) : ta.rma(high - low, lengthk)
upper = ma + rangema * mult
lower = ma - rangema * mult

//stoch
periodK = input.int(10, title="%K Length", minval=1,group="Stochastic")
smoothK = input.int(1, title="%K Smoothing", minval=1,group="Stochastic")
periodD = input.int(1, title="%D Smoothing", minval=1,group="Stochastic")
k = ta.sma(ta.stoch(close, high, low, periodK), smoothK)
d = ta.sma(k, periodD)

//macd 1
fast_length = input(title="Fast Length MACD", defval=4,group="MACD Fast")
slow_length = input(title="Slow Length MACD", defval=34,group="MACD Fast")
signal_length = input.int(title="Signal Smoothing MACD",  minval = 1, maxval = 50, defval = 5,group="MACD Fast")
sma_source = input.string(title="Oscillator MA Type MACD",  defval="EMA", options=["SMA", "EMA"],group="MACD Fast")
sma_signal = input.string(title="Signal Line MA Type MACD", defval="EMA", options=["SMA", "EMA"],group="MACD Fast")

fast_ma = sma_source == "SMA" ? ta.sma(src, fast_length) : ta.ema(src, fast_length)
slow_ma = sma_source == "SMA" ? ta.sma(src, slow_length) : ta.ema(src, slow_length)
macd = fast_ma - slow_ma
signal = sma_signal == "SMA" ? ta.sma(macd, signal_length) : ta.ema(macd, signal_length)
hist = macd - signal




long= close > out and close < upper and close > lower and hist < 0 and k < 50 and close > out2 

short= close < out and close < upper and close > lower and hist > 0 and k > 50 and close < out2 

strategy.entry("long",strategy.long,when= long)

strategy.entry("short",strategy.short,when=short)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-22. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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