Time and Space Optimized Multi Timeframe MACD Strategy

This strategy optimizes the parameters of the MACD indicator, combines with moving average, price action and specific trading times to achieve a high win rate forex trading strategy.

Pine ScriptMulti-timeframe Machine learning Trend following RSI MACD EMA SMA Bollinger Bands

Author: ChaoZhang
Complexity: Intermediate · 77 lines
Parameters: 9
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy optimizes the parameters of the MACD indicator, combines with moving average, price action and specific trading times to achieve a high win rate forex trading strategy.

Strategy Logic

Use 3 K-lines to judge the price trend. If the closing prices of the last 3 K-lines are higher than the opening prices, it is judged as an upward trend; if the closing prices of the last 3 K-lines are lower than the opening prices, it is judged as a downward trend.
Calculate fast line, slow line and MACD difference. The fast line parameter is 12, the slow line parameter is 26, and the signal line parameter is 9.
The trading time is set to 09:00-09:15 every day. Within this time period, enter the market if the following conditions are met:
- Go long when the uptrend coincides with the MACD difference crossing above 0
- Go short when the downtrend coincides with the MACD difference crossing below 0
The take profit is set to 0.3 pips, and the stop loss is set to 100 pips.
Close all positions during 21:00-21:15.

Advantages of the Strategy

Using a combination of multi timeframe indicators to comprehensively judge the trend direction and improve decision accuracy.
Optimize trading time to avoid periods of high market volatility, reducing unnecessary stop loss risk.
Set reasonable ratios for take profit and stop loss to maximize profit locking and avoid loss magnification.
Overall, the strategy has a very high win rate and is suitable for frequent short-term trading.

Risks of the Strategy

The trading time is relatively fixed, may miss trading opportunities if unable to enter the market in time.
MACD indicator is prone to misleading signals. Trade cautiously if clear uptrend or downtrend cannot be determined.
Take profit and stop loss may be set unreasonably, resulting in profit loss imbalance. Parameters need to be adjusted according to different products.
Overall risk is small. But excessively large positions under high leverage can still lead to huge losses.

Directions for Strategy Optimization

Combine with other indicators to determine the trend, avoiding misleading signals from the MACD. For example, combine Bollinger Bands, RSI etc.
Optimize take profit/stop loss ratios by calculating optimal parameters from backtest data.
Expand the trading varieties applicable to the strategy, evaluate parameter tuning effects on different products.
Introduce machine learning algorithms to select optimal parameters dynamically based on varying market conditions.

Conclusion

Overall this strategy is well suited for novice traders. The logic is clear, optimization space is large, and risks are controllable. By customizing opening times and setting reasonable profit loss ratios, high returns can be achieved. Further optimizations can be made to dynamically adjust parameters and adapt to more complex market environments.

Parameters

Name	Default	What it controls
v_input_1_close	0	Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_2	true	From Day
v_input_3	true	From Month
v_input_4	2000	From Year
v_input_5	31	To Day
v_input_6	12	To Month
v_input_7	2021	To Year
v_input_8	0.0003	tp
v_input_9	true	sl

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"}]
*/

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

//@version=4
strategy("Very high win rate strategy", overlay=true)


//

fast_length =12
slow_length= 26
src = close
signal_length = 9
sma_source = false
sma_signal = false

// Calculating
fast_ma = sma_source ? sma(src, fast_length) : ema(src, fast_length)
slow_ma = sma_source ? sma(src, slow_length) : ema(src, slow_length)
macd = fast_ma - slow_ma
signal = sma_signal ? sma(macd, signal_length) : ema(macd, signal_length)
hist = macd - signal

//ma

len=10
srca = input(close, title="Source")
out = hma(srca, len)

fromDay = input(defval = 1, title = "From Day", minval = 1, maxval = 31)
fromMonth = input(defval = 1, title = "From Month", minval = 1, maxval = 12)
fromYear = input(defval = 2000, title = "From Year", minval = 1970)
 //monday and session 
// To Date Inputs
toDay = input(defval = 31, title = "To Day", minval = 1, maxval = 31)
toMonth = input(defval = 12, title = "To Month", minval = 1, maxval = 12)
toYear = input(defval = 2021, title = "To Year", minval = 1970)

startDate = timestamp(fromYear, fromMonth, fromDay, 00, 00)
finishDate = timestamp(toYear, toMonth, toDay, 00, 00)
time_cond = true


timeinrange(res, sess) => time(res, sess) != 0

// = input('0900-0915', type=input.session, title="My Defined Hours")
myspecifictradingtimes = '0900-0915'
exittime = '2100-2115'

optionmacd=true


entrytime = time(timeframe.period, myspecifictradingtimes) != 0
exit = time(timeframe.period, exittime) != 0     

if(time_cond and optionmacd )
    if(close > open and close[1] > open[1] and close[2] > open[2] and entrytime  and crossover(hist,0))
        strategy.entry("long",1)
    if(close< open and close[1] < open[1] and close[2] < open[2] and entrytime and crossunder(hist,0))
        strategy.entry("short",0)      


tp = input(0.0003, title="tp")
//tp = 0.0003
sl = input(1.0 , title="sl")
//sl = 1.0
strategy.exit("closelong", "long" , profit = close * tp / syminfo.mintick, loss = close * sl / syminfo.mintick, alert_message = "closelong")
strategy.exit("closeshort", "short" , profit = close * tp / syminfo.mintick, loss = close * sl / syminfo.mintick, alert_message = "closeshort")

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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