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Simple Strategy - New to this
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Hey guys!
I'm trying out my first code on Backtrader, been trading with algo for some time but never backtested! Wanted to find the best strategy to use Binance Futures Leverage and Coumpounding long-term to exponentially grow my account. I know it seems difficult, but I've had some succesfull strategies, just want to know how to test several of them!
In order to do that, my objective for now is to compute a strategy and test it in several coins, it has dependencies on BTC fluctuation and will generate buy's of other coins. But first things come first, for now I want to do the following:
- Buy when SMA fast (20) > SMA slow (50) and RSI < 30
- Sell when profit is 0.6% OR loss is 0.2% (adding the comission of 0.06%)
In my code the truth is that I'm not even being able to get the BUY going, I get the following:
# 2021-07-07, BUY CREATE 0.084140 # EXECUTED PRICE IS: 0.000000I guess the buy Market Order is not even buying, it just stays there
I'll leave the code so that you guys can comment on it, If anyone is open to developing an idea with me let me know, I would love to share my insights on the crypto world.
from datetime import datetime import os import matplotlib import threading import backtrader as bt import backtrader.feeds as btfeeds from binancedata import * # Overall account balance account_balance = 100 # size of each trade in native currency # ie if backtesting on ETH, each trade will be 1ETH in size trade_size = 50 # in % take_profit = 0.6 stop_loss = 0.2 # The difference betweent the current candle price compared to the previous one # If higher than 1% we will buy buy_trigger = 1 # Get historical data and store filename # No need to run this if you already have historical data # Simply comment out and re-assign your filename to 'ETHUSDT_1 Jan 2021.csv' for example threads = [] coins = get_coins() for coin in coins: t = threading.Thread(target=get_historical_data, args=(coin, '7 Jul 2021', Client.KLINE_INTERVAL_1MINUTE) ) #'get_historical_data('ETHUSDT', '1 Jan 2021', Client.KLINE_INTERVAL_1MINUTE) t.start() threads.append(t) [thread.join() for thread in threads] def get_all_filenames(): for coin in coins: return [get_historical_data(coin, '7 Jul 2021', Client.KLINE_INTERVAL_1MINUTE) for coin in coins] # Create a Stratey class TestStrategy(bt.Strategy): # Moving average parameters params = ( ('verbose', False), ('maperiod', 14), ('quantity', None), ('upper', 70), # upper threshold ('lower', 30), # lower threshold ('pfast', 20), ('pslow', 50) # stop loss % ) def log(self, txt, dt=None): ''' Logging function fot this strategy''' dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close # To keep track of pending orders self.order = None self.slow_sma = bt.indicators.MovingAverageSimple(self.datas[0], period=self.params.pslow) self.fast_sma = bt.indicators.MovingAverageSimple(self.datas[0], period=self.params.pfast) # # bt.indicators.WeightedMovingAverage(self.datas[0], period=25, # subplot=True) # bt.indicators.StochasticSlow(self.datas[0]) # Add a MovingAverageSimple indicator self.rsi = bt.indicators.RSI_SMA(self.datas[0], period=self.params.maperiod) bt.indicators.MACDHisto(self.datas[0]) rsi = bt.indicators.RSI(self.datas[0]) # bt.indicators.SmoothedMovingAverage(rsi, period=10) # bt.indicators.ATR(self.datas[0], plot=False) def notify_order(self, order): if order.status in [order.Submitted, order.Accepted]: # Buy/Sell order submitted/accepted to/by broker - Nothing to do return # Check if an order has been completed # Attention: broker could reject order if not enough cash if order.status in [order.Completed]: if order.isbuy(): self.log('BUY EXECUTED, %.6f' % order.executed.price) elif order.issell(): self.log('SELL EXECUTED, %.6f' % order.executed.price) self.bar_executed = len(self) elif order.status in [order.Canceled, order.Margin, order.Rejected]: self.log('Order Canceled/Margin/Rejected') # Write down: no pending order self.order = None def next(self): # Simply log the closing price of the series from the reference self.log('Close, %.6f' % self.dataclose[0]) # Check if an order is pending ... if yes, we cannot send a 2nd one if self.order: return # Check if we are in the market if not self.position: # Not yet ... we MIGHT BUY if ... if self.fast_sma[0] > self.slow_sma[0] and self.rsi < self.params.lower: self.log(f'BUY CREATE {self.dataclose[0]:6f}') # Keep track of the created order to avoid a 2nd order self.buy(size=trade_size, exectype=bt.Order.Market) self.order = self.buy() print(f'EXECUTED PRICE IS: %.6f' % self.order.executed.price) if self.position: if self.dataclose[0] > self.position.price + (self.position.price*take_profit/100) or self.dataclose[0] < self.position.price - (self.position.price*stop_loss/100): #self.log('SELL CREATE, %.2f' % self.dataclose[0]) #self.sell(size=trade_size, exectype=bt.Order.Market) #self.order = self.sell() self.close() self.order = self.close() def start_backtesting(): historical_data = get_all_filenames() for coin in historical_data: # Create a cerebro entity cerebro = bt.Cerebro() # Add a strategy cerebro.addstrategy(TestStrategy) # load data from our CSV file data = btfeeds.GenericCSVData( # Create a Data Feed dataname=coin, fromdate=datetime(2021, 7, 7), todate=datetime(2021, 7, 8), nullvalue=0.0, dtformat=lambda x: datetime.utcfromtimestamp(float(x) / 1000.0), datetime=0, high=1, low=2, open=3, close=4, volume = -1, openinterest=-1 ) # Add the Data Feed to Cerebro cerebro.adddata(data) # Set our desired cash start cerebro.broker.setcash(account_balance) # Print out the starting conditions print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) # Run over everything cerebro.run() cerebro.plot() # Print out the final result print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue()) start_backtesting()Thank you!