Monthly Close Price and Moving Average Crossover Strategy

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…

Pine ScriptTrend following Machine learning Mean reversion MACD EMA SMA

Author: ChaoZhang
Complexity: Intermediate · 87 lines
Parameters: 13
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

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.

Parameters

Name	Default	What it controls
v_input_1	true	Show MA
v_input_2	true	Exponential MA
v_input_3	false	Other ticker MA
v_input_4	BTC_USDT:swap	Ticker MA
v_input_5	M	Time MA (W, D, [min])
v_input_6	8	Period MA
v_input_7	false	Show Date Range
v_input_8	true	From Month
v_input_9	true	From Day
v_input_10	1995	From Year
v_input_11	true	Thru Month
v_input_12	true	Thru Day
v_input_13	2112	Thru Year

Source Pine Script

/*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')

Published by ChaoZhang on the FMZ strategy square · last modified 2023-11-23. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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