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Strategy vault / Other

Averaging Strategy

When encountering a faith coin but worried about holding it, use an averaging strategy: fully auto high sell and low buy, always keeping the value of the coin and U balanced. Only supports cryptocurrency spot One…

PythonOther
Author
去者伯仁
Complexity
Intermediate · 92 lines
Parameters
0

When encountering a faith coin but worried about holding it, use an averaging strategy: fully auto high sell and low buy, always keeping the value of the coin and U balanced. Only supports cryptocurrency spot One percent of your funds must be able to buy the minimum transaction amount of the currency

Source Python

'''backtest
start: 2020-04-03 00:00:00
end: 2021-04-02 23:59:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Binance","currency":"BTC_USDT","stocks":0}]
'''

import time
class juncang_strategy():  
    def __init__(self,exchange):
        self.p = 0.5
        self.account = None
        self.cny = 0
        self.btc = 0
        self.exchange =exchange
        #All of the aboveselfobject properties

    def cancelAllOrders(self):
        orders = _C(self.exchange.GetOrders)
        for order in orders:
            exchange.CancelOrder(order['Id'], order)
        return True


    def balanceAccount(self):
        self.cancelAllOrders()

        kr =  _C(self.exchange.GetRecords,PERIOD_M1)
        account = _C(self.exchange.GetAccount)
        if account is None:
            return

        #Assignment
        self.account = account

        #Assignment
        self.btc = account.Stocks+account.FrozenStocks
        self.cny = account.Balance+account.FrozenBalance
        
        accountmoney=self.btc * kr[-1].Close + self.cny
        self.p = self.btc * kr[-1].Close / accountmoney
        # Log(self.p)
        tradenum=accountmoney/kr[-1].Close/100
        if tradenum<0.001:
            tradenum=0.001
        #Judgmentself.pWhether the value is less than0.48
        Log(self.p)
        if (0.45<self.p < 0.49):
            #CallLogFunction and pass in parameters"Start balancing", self.p
            Log("Start balancing", self.p)

            self.exchange.Buy(kr[-1].Close, tradenum)

            Log("Number of Coins Held:",self.btc,"Amount of Cash:",self.cny)

            #Judgmentself.pWhether the value is greater than0.52
        elif (0.55 > self.p > 0.51):
            #CallLogFunction and pass in parameters"Start balancing", self.p
            Log("Start balancing", self.p)

            #CallSellFunction and pass in the corresponding parameters
            self.exchange.Sell(kr[-1].Close, tradenum)

            Log("Number of Coins Held:",self.btc,"Amount of Cash:",self.cny)
        elif (self.p >= 0.55):
            #CallLogFunction and pass in parameters"Start balancing", self.p
            Log("Start balancing,Quick close position", self.p)

            self.exchange.Sell(kr[-1].Close, tradenum*10)

            Log("Number of Coins Held:",self.btc,"Amount of Cash:",self.cny)
        elif (self.p <= 0.45):
            #CallLogFunction and pass in parameters"Start balancing", self.p
            Log("Start balancing, quick build position", self.p)

            self.exchange.Buy(kr[-1].Close, tradenum*10)

            Log("Number of Coins Held:",self.btc,"Amount of Cash:",self.cny)




#Functionmain
def main():
    #reaper Is Instance of Constructor
    reaper = juncang_strategy(exchange)
    while (True):
        #Call through instancepollMethod
        reaper.balanceAccount()
        Sleep(1000*10)

Published by 去者伯仁 on the FMZ strategy square · last modified 2021-05-12. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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in Claude Code, Cursor, Codex & 4 more
/agenkit build a production-grade trading bot with a backtest engine, risk controls and paper trading by default from strategies/Averaging-Strategy.md
  1. npx agenkit install engineering-kit in your project
  2. Paste the command into your agent and approve each phase
  3. Get tests, backtest, risk controls and a paper-trading switch

Understand it first

Before you run this, read the lesson behind the idea and how to backtest it honestly. Specs like this one are the survivors of an unknown number of trials; deflate their results accordingly.

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