Dynamic Pattern Trend Reversal Strategy

Pine ScriptMean reversion Trend following Breakout MACD SMA

Author: ChaoZhang
Complexity: Intermediate · 99 lines
Parameters: 2
Backtest bar: 1m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The Dynamic Pattern Trend Reversal strategy uses linear regression to predict prices and moving average lines to form pattern for generating trading signals. It produces buy signals when the predicted price crosses above the moving average line upwards and sell signals when crossing below downwards, capturing trend reversals.

Strategy Logic

Calculate linear regression of stock price based on trading volume to obtain predicted price
Compute moving averages under diferentes conditions
Generate buy signal when predicted price crosses moving average upwards
Generate sell signal when predicted price crosses moving average downwards
Incorporate MACD indicator to determine timing of trend reversal

The combination of above signals with multiple confirmations avoids false breakouts and improves accuracy.

Advantage Analysis

Utilize linear regression to predict price trend, enhancing signal accuracy
Capture trend reversals via moving average patterns
Regression based on trading volume has better economics meaning
Multiple confirmations by MACD etc. reduce false signals

Risk Analysis

Parameters of linear regression significantly impact results
Moving average settings also affect signal quality
Despite having confirmations, false signals remain a risk
Code can be further optimized to reduce trade frequency and improve profit rate

Optimization Directions

Optimize parameters of linear regression and moving averages
Add more confirmation conditions to lower false signal rates
Incorporate more factors to judge quality of trend reversals
Enhance stop loss strategies to reduce risks for individual trades

Conclusion

The Dynamic Pattern Trend Reversal strategy integrates linear regression prediction and moving average patterns to capture trend reversals. Compared to single indicator strategies, it has higher reliability. Further improvements on parameters, confirmations and other optimizations can enhance signal quality and profitability.

Parameters

Name	Default	What it controls
v_input_1	21	Strategy Length
v_input_2	9	Linear Lookback

Source Pine Script

/*backtest
start: 2023-12-05 00:00:00
end: 2023-12-12 00:00:00
period: 1m
basePeriod: 1m
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/
// © stocktechbot
//@version=5
strategy("Linear Cross", overlay=true, margin_long=100, margin_short=0)

//Linear Regression

vol = volume

// Function to calculate linear regression
linregs(y, x, len) =>
    ybar = math.sum(y, len)/len
    xbar = math.sum(x, len)/len
    b = math.sum((x - xbar)*(y - ybar),len)/math.sum((x - xbar)*(x - xbar),len)
    a = ybar - b*xbar
    [a, b]

// Historical stock price data
price = close

// Length of linear regression
len = input(defval = 21, title = 'Strategy Length')
linearlen=input(defval = 9, title = 'Linear Lookback')
[a, b] = linregs(price, vol, len)

// Calculate linear regression for stock price based on volume
//eps = request.earnings(syminfo.ticker, earnings.actual)
//MA For double confirmation

out = ta.sma(close, 200)
outf = ta.sma(close, 50)
outn = ta.sma(close, 90)
outt = ta.sma(close, 21)
outthree = ta.sma(close, 9)

// Predicted stock price based on volume
predicted_price = a + b*vol

// Check if predicted price is between open and close
is_between = open < predicted_price and predicted_price < close

//MACD
//[macdLine, signalLine, histLine] = ta.macd(close, 12, 26, 9)

// Plot predicted stock price
plot(predicted_price, color=color.rgb(65, 59, 150), linewidth=2, title="Predicted Price")
plot(ta.sma(predicted_price,linearlen), color=color.rgb(199, 43, 64), linewidth=2, title="MA Predicted Price")
//offset = input.int(title="Offset", defval=0, minval=-500, maxval=500)
plot(out, color=color.blue, title="MA200")
[macdLine, signalLine, histLine] = ta.macd(predicted_price, 12, 26, 9)

//BUY Signal

longCondition=false
mafentry =ta.sma(close, 50) > ta.sma(close, 90)
//matentry = ta.sma(close, 21) > ta.sma(close, 50)
matwohun = close > ta.sma(close, 200)
twohunraise = ta.rising(out, 2)
twentyrise = ta.rising(outt, 2)
macdrise = ta.rising(macdLine,2)
macdlong = ta.crossover(predicted_price, ta.wma(predicted_price,linearlen))  and (signalLine < macdLine)
if macdlong and macdrise
    longCondition := true

if (longCondition)
    strategy.entry("My Long Entry Id", strategy.long)
//Sell Signal
lastEntryPrice = strategy.opentrades.entry_price(strategy.opentrades - 1)
daysSinceEntry = len
daysSinceEntry := int((time - strategy.opentrades.entry_time(strategy.opentrades - 1)) / (24 * 60 * 60 * 1000))
percentageChange = (close - lastEntryPrice) / lastEntryPrice * 100
//trailChange = (ta.highest(close,daysSinceEntry) - close) / close * 100

//label.new(bar_index, high, color=color.black, textcolor=color.white,text=str.tostring(int(trailChange)))
shortCondition=false
mafexit =ta.sma(close, 50) < ta.sma(close, 90)
matexit = ta.sma(close, 21) < ta.sma(close, 50)
matwohund = close < ta.sma(close, 200)
twohunfall = ta.falling(out, 3)
twentyfall = ta.falling(outt, 2)
shortmafall = ta.falling(outthree, 1)
macdfall = ta.falling(macdLine,1)
macdsell = macdLine < signalLine
if macdfall and macdsell and (macdLine < signalLine) and ta.falling(low,2)
    shortCondition := true

if (shortCondition)
    strategy.entry("My Short Entry Id", strategy.short)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-13. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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