Accumulation Stage Identifier and Trading Strategy

Pine ScriptMachine learning Mean reversion Momentum MACD SMA ATR OBV

Author: ChaoZhang
Complexity: Intermediate · 119 lines
Parameters: 4
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy combines moving averages, volume indicators and price momentum indicators to design a set of quantitative rules to identify the timing of stocks entering the accumulation stage. At this stage, stocks are usually in a price consolidation and storage state, providing a good low-price opportunity.

Strategy Principle

The strategy uses 50-day, 90-day and 200-day simple moving averages to determine price trends. Buy signals are only generated when prices are above the 200-day line. This filters out the uncertainty of major downtrends.

In addition to judging the major trend, the strategy also judges the order of short-term moving averages to confirm the trend. Specifically, it judges if the 50-day line is above the 90-day line.

On the basis that the moving average confirms the major and short-term trends, the strategy combines the PVT volume indicator and the MACD indicator to judge the accumulation characteristics. Buy signals are generated only when the PVT breaks upward, the MACD line is higher than the Signal line, and the volume expands.

Advantages

Compared with using moving averages alone, this strategy also checks the characteristics of volume while confirming the trend. This can more accurately determine the timing of stocks entering the accumulation stage, thus ensuring better entry prices.

By analyzing multiple time frames, this strategy combines medium- and long-term trend judgments and short-term feature judgments to match time frames, which can reduce uncertainty from judging a single time frame incorrectly.

Risks and Solutions

This strategy relies mainly on moving average judgments. When prices fluctuate violently, moving average judgments will fail. At this point, the position size should be reduced or a stop loss exit should be triggered.

In addition, misjudgement on the accumulation stage is possible, thus missing reversal opportunities. This requires observing more feature indicators to confirm judgements.

Optimization Ideas

Machine learning algorithms can be introduced into this strategy by extracting features and model training to achieve automatic judgment of the accumulation stage. This can reduce limitations from manually setting thresholds.

In addition, this strategy can also try the breakpoint functionality to automatically switch parameters under different market environments, making the strategy more robust.

Summary

In summary, this strategy generally adopts the idea of matching prices and volumes to judge the characteristics of stock accumulation stages. While confirming the major direction, it digs short-term accumulation opportunities. There is still room for further improvement in strategy performance by introducing parameter optimization and machine learning.

Parameters

Name	Default	What it controls
v_input_1	21	Lookback
v_input_int_1	false	Offset
v_input_2	21	Entry Line
v_input_3	21	Exit Line

Source Pine Script

/*backtest
start: 2023-02-13 00:00:00
end: 2024-02-19 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/
// © stocktechbot

//@version=5
strategy("Accumulate", overlay = true)
lookback = input(defval = 21, title = 'Lookback')
offset = input.int(title="Offset", defval=0, minval=-500, maxval=500)
//SMA Tredline
out = ta.sma(close, 200)
outf = ta.sma(close, 50)
outn = ta.sma(close, 90)
outt = ta.sma(close, 21)
//sma plot
plot(out, color=color.blue, title="MA200", offset=offset)
plot(outf, color=color.maroon, title="MA50", offset=offset)
plot(outn, color=color.orange, title="MA90", offset=offset)
plot(outt, color=color.olive, title="MA21", offset=offset)

//MarketCap Calculation
//MarketCap = 0.0
//TSO = request.financial(syminfo.tickerid, "TOTAL_SHARES_OUTSTANDING", "FQ", ignore_invalid_symbol = true)


//if str.tostring(TSO) != 'na'
//    if ta.barssince(TSO != TSO[1] and TSO > TSO[1])==0
//        MarketCap := TSO * close
//       
//    if barstate.islast and MarketCap == 0
//        runtime.error("No MarketCap is provided by the data vendor.")
//    
//momlen = 100
//msrc = MarketCap
//mom = msrc - msrc[momlen]
//plotmom = if (mom > mom[1])
//    true
//else
//   false

//OBV with sma on macd
obv = ta.cum(math.sign(ta.change(close)) * volume)
smoothingLength = 5
smoothingLine = ta.sma(obv,5)
[macdLine, signalLine, histLine] = ta.macd(ta.pvt, 12, 26, 9)
sellvolhigh = macdLine < signalLine
buyvolhigh = macdLine > signalLine
//Buy Signal
mafentry =ta.sma(close, 50) > ta.sma(close, 90)
//matentry = ta.sma(close, 21) > ta.sma(close, 50)
matwohun = close > ta.sma(close, 200)
higheshigh = ta.rising(high, 2)
higheslow = ta.rising(low, 2 )
twohunraise = ta.rising(out, 2)
//highvol =  ta.crossover(volume, ta.sma(volume, lookback))
highvol = ta.rising(volume,2)
fourlow = ta.lowest(close, lookback)
fourhig = ta.highest(close, lookback)
change =  (((fourhig - fourlow) / fourlow) * 100) <= 30
green = close > open
allup = false
lineabove = ta.cross(close, ta.sma(close, input(defval = 21, title = 'Entry Line')))
if matwohun and mafentry and higheshigh and twohunraise and buyvolhigh
//if higheshigh and higheslow and highvol
    allup := true

plotshape(allup, style=shape.arrowup,location=location.belowbar, color=color.green, title = "Buy Signal")

barsSinceLastEntry() =>
    strategy.opentrades > 0 ? bar_index - strategy.opentrades.entry_bar_index(strategy.opentrades - 1) : na
    
//Sell Signal
mafexit =ta.sma(close, 50) < ta.sma(close, 90)
matexit = ta.sma(close, 21) < ta.sma(close, 50)
matwohund = close < ta.sma(close, 200)
linebreak = ta.sma(close, input(defval = 21, title = 'Exit Line')) > close
lowesthigh = ta.falling(high, 3)
lowestlow = ta.falling(low, 2 )
twohunfall = ta.falling(out, 3)
twentyfall = ta.falling(outt, 2)
highvole =  ta.crossover(volume, ta.sma(volume, 5))
//fourlow = ta.lowest(close, lookback)
//fourhig = ta.highest(close, lookback)
changed =  (((fourhig - close) / close) * 100) >= 10
red = close < open
atr = ta.atr(14)
//atrsmalen = int(bar_index - strategy.opentrades.entry_bar_index(strategy.opentrades - 1) )
atrsmalen = barsSinceLastEntry()
atrsma = false
atrlen = 5
if str.tostring(atrsmalen) != 'NaN' and atrsmalen > 0
    atrlen := atrsmalen

    
atrsma := atr > ta.sma(atr,50)


alldwn = false
if sellvolhigh and lowestlow and (close < close[1] and close < open)
//if higheshigh and higheslow and highvol
    alldwn := true

plotshape(alldwn, style=shape.arrowdown,location=location.abovebar, color=color.red, title = "Sell Signal")


longCondition = ta.crossover(ta.sma(close, 14), ta.sma(close, 28))
if (allup)
    strategy.entry("My Long Entry Id", strategy.long)

shortCondition = ta.crossunder(ta.sma(close, 14), ta.sma(close, 28))
if (alldwn)
    strategy.entry("My Short Entry Id", strategy.short)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-02-20. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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