dkav6

class MLStrategy(Strategy):
  n1=5
  n2=10

  def init(self):
    self.sma1 = self.I(SMA, self.data.Close, self.n1)
    self.sma2 = self.I(SMA, self.data.Close, self.n2)

  def next(self):
    if crossover(self.sma1, self.sma2) and self.data.target == 1:

dkav6

from backtesting import Backtest, Strategy
from backtesting.lib import crossover
from backtesting.test import SMA

class MLStrategy(Strategy):
  n1=5
  n2=10

  def init(self):
    self.sma1 = self.I(SMA, self.data.Close, self.n1)

dkav6

def get_ohlc(ticker, period='60d', interval='5m'):
  cols = ['Open','High','Low','Close']
  ohlc = Ticker(ticker).history(period=period, interval=interval)[cols][:-1]
  return ohlc

df = get_ohlc('ETH-USD')

dkav6

def run(request):
    alert = request.get_json()
    # # If alert request is a buy, we buy
    symbol = alert['ticker']
    pair = symbol + "-USD"
    side = alert['side']
    usd_price = client.fetch_ticker(pair)['last']
    position = client.fetch_balance()[symbol]['free']
    size = usd_price * position
    if side == 'buy' and size < 1:

dkav6

def buy_max(pair, client=client):
    # Use max possible usd size
    price = client.fetchTicker(pair)['last']
    size = client.fetchBalance()['USD']['free'] / price
    client.createMarketBuyOrder(pair, amount=size)
    print(f"bought ${size} of {pair}")

def sell_max(pair, client=client):
    symbol = pair.split("-")[0]
    size = client.fetchBalance()[symbol]['free']

dkav6

from ccxt import coinbasepro

def get_client():
    client = coinbasepro({
        'apiKey': '',
        'secret': '',
        'password': '',
        'enableRateLimit': True,
    })
    return client

dkav6

df = get_data('ETH-USD')
df = df/1000
strategies = [SmaCross, EmaCross, WmaCross]
all_runs = []
for strategy in strategies:
  bt = Backtest(df, strategy, cash=100, commission=.002)
  stats = bt.optimize(n1=range(5, 90, 5),
                      n2=range(10, 120, 5),
                      maximize='Return (Ann.) [%]',
                      constraint=lambda param: param.n1 < param.n2)

dkav6

def get_data(pair):
  ticker = Ticker(pair)
  df = ticker.history(period='3y', rounding=True)[['Open','High','Low','Close']]
  return df

dkav6

class EmaCross(Strategy):
    # Define the two EMA lags as *class variables*
    # for later optimization
    n1 = 5
    n2 = 10
    
    def init(self):
        # Precompute two moving averages
        self.ema1 = self.I(EMA, self.data.Close, self.n1)
        self.ema2 = self.I(EMA, self.data.Close, self.n2)

dkav6

from backtesting import Strategy
from backtesting.lib import crossover
from backtesting import Backtest
from yfinance import Ticker
import pandas as pd
from talib import EMA, SMA, WMA