Monthly Close Price and Moving Average Crossover Strategy
Author: ChaoZhang, Date: 2023-11-23 17:09:01Tags:
Overview
This strategy generates trading signals based on the crossover between the monthly close price and moving average lines. It goes long when the monthly close price crosses above the moving average, and flats when the monthly close price crosses below the moving average.
Strategy Logic
The core logic of this strategy is:
- Take the moving average period parameter as input. Choose between SMA and EMA.
- Option to display the moving average line.
- Option to use another ticker’s close price as signal source.
- Determine trading signals based on relationship between monthly close price and moving average:
- Close price crossing above MA - Long
- Close price crossing below MA - Close long position
This strategy utilizes the smoothing capability of moving averages to filter out price noises and capture mid-term trend reversals. Crossing above the MA suggests an emerging bull trend while crossing below indicates the trend is turning bearish.
Advantages
The main advantages of this strategy are:
- Uses monthly data to effectively filter out intraday noises and capture mid-long term trends
- Customizable MA period for optimization across different tickers
- Option to use another ticker as signal source improves stability
- Implements advanced anti-repainting techniques
- Flexible backtesting time frame for ease of testing
In summary, this is a simple yet practical strategy framework that can be adapted to most stocks through parameter tuning, especially suitable for mid-long term investors.
Risks
There are also a few risks to note:
- Monthly data updates slowly, unable to reflect price changes in real-time
- Lags behind and could miss short-term opportunities
- MAs have inherent lags, signal timing unpredictable
- Suboptimal parameter selection leads to over-conservatism or missed opportunities
Suggested ways to mitigate risks:
- Incorporate faster timeframe technical indicators for auxiliary judgement
- Optimize MA period to find best parameters
- Use more stable benchmark as signal source
- Adjust position sizing to limit losses
Enhancement Opportunities
This strategy has great potential for enhancement:
- Incorporate stop loss to lock in profits and control risks
- Add complementing indicators like KD, MACD to improve signal accuracy
- Employ machine learning techniques to dynamically optimize MA parameters
- Introduce position sizing that aligns with trends
- Build in long/short switching capabilities based on market conditions
- Merge with faster timeframe prices for quicker reactions
Conclusion
The monthly close and MA crossover strategy has simple, straightforward logic and can be adapted to various tickers through parameter tuning. It is especially suitable for mid-long term investors. With the continuing enhancement of stop loss, parameter optimization and other modules, this strategy shows great promise.
/*backtest
start: 2022-11-16 00:00:00
end: 2023-11-22 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/
// © universique
//@version=4
strategy("Monthly MA Close ", shorttitle="MMAC", overlay=true, default_qty_type = strategy.percent_of_equity, default_qty_value = 100)
//MAY 6 2020 18:00
// No repaint function
// Function to securely and simply call `security()` so that it never repaints and never looks ahead.
f_secureSecurity(_symbol, _res, _src) => security(_symbol, _res, _src[1], lookahead = barmerge.lookahead_on)
//sec10 = f_secureSecurity(syminfo.tickerid, higherTf, data)
// ————— Converts current chart resolution into a float minutes value.
f_resInMinutes() =>
_resInMinutes = timeframe.multiplier * (
timeframe.isseconds ? 1. / 60 :
timeframe.isminutes ? 1. :
timeframe.isdaily ? 60. * 24 :
timeframe.isweekly ? 60. * 24 * 7 :
timeframe.ismonthly ? 60. * 24 * 30.4375 : na)
// ————— Returns the float minutes value of the string _res.
f_tfResInMinutes(_res) =>
// _res: resolution of any TF (in "timeframe.period" string format).
// Dependency: f_resInMinutes().
security(syminfo.tickerid, _res, f_resInMinutes())
// —————————— Determine if current timeframe is smaller that higher timeframe selected in Inputs.
// Get higher timeframe in minutes.
//higherTfInMinutes = f_tfResInMinutes(higherTf)
// Get current timeframe in minutes.
currentTfInMinutes = f_resInMinutes()
// Compare current TF to higher TF to make sure it is smaller, otherwise our plots don't make sense.
//chartOnLowerTf = currentTfInMinutes < higherTfInMinutes
// Input
switch1=input(true, title="Show MA")
exponential = input(true, title="Exponential MA")
ticker = input(false, title="Other ticker MA")
tic_ma = input(title="Ticker MA", type=input.symbol, defval="BTC_USDT:swap")
res_ma = input(title="Time MA (W, D, [min])", type=input.string, defval="M")
len_ma = input(8, minval=1, title="Period MA")
ma_cus = exponential?f_secureSecurity(tic_ma, res_ma, ema(close,len_ma)) : f_secureSecurity(tic_ma, res_ma, sma(close,len_ma))
ma_long = exponential?f_secureSecurity(syminfo.tickerid, res_ma, ema(close,len_ma)) : f_secureSecurity(syminfo.tickerid, res_ma, sma(close,len_ma))
cl1 = f_secureSecurity(syminfo.tickerid, 'M', close)
cl2 = f_secureSecurity(tic_ma, 'M', close)
// Input Backtest Range
showDate = input(defval = false, title = "Show Date Range", type = input.bool)
fromMonth = input(defval = 1, title = "From Month", type = input.integer, minval = 1, maxval = 12)
fromDay = input(defval = 1, title = "From Day", type = input.integer, minval = 1, maxval = 31)
fromYear = input(defval = 1995, title = "From Year", type = input.integer, minval = 1850)
thruMonth = input(defval = 1, title = "Thru Month", type = input.integer, minval = 1, maxval = 12)
thruDay = input(defval = 1, title = "Thru Day", type = input.integer, minval = 1, maxval = 31)
thruYear = input(defval = 2112, title = "Thru Year", type = input.integer, minval = 1850)
// Funcion Example
start = timestamp(fromYear, fromMonth, fromDay, 00, 00) // backtest start window
finish = timestamp(thruYear, thruMonth, thruDay, 23, 59) // backtest finish window
window() => time >= start and time <= finish ? true : false // create function "within window of time"
// Calculation
bullish_cross = ticker?cl2>ma_cus : cl1>ma_long
bearish_cross = ticker?cl2<ma_cus : cl1<ma_long
MAColor = bullish_cross ? color.green : bearish_cross ? color.red : color.orange
// Strategy
strategy.entry("long", strategy.long, when = window() and bullish_cross)
strategy.close("long", when = window() and bearish_cross)
// Output
plot(switch1?ma_long:na,color = MAColor,linewidth=4)
// Alerts
alertcondition(bullish_cross, title='Bullish', message='Bullish')
alertcondition(bearish_cross, title='Bearish', message='Bearish')
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