Key features:

• Event-driven backtesting using Python
• 1-minute US stock data included
• Support for equities and futures
• Integrated support for related open-source libraries including Alphalens, Pyfolio, and QGrid
• Live trading with QuantRocket-built adapters. Learn more

import zipline.api as algo

def handle_data(context, data):
    short_mavg = data.history(context.sym, 'price', 100, '1d').mean()
    long_mavg = data.history(context.sym, 'price', 300, '1d').mean()

    if short_mavg > long_mavg:
        algo.order_target_percent(context.sym, 0.2)
    elif short_mavg < long_mavg:
        algo.order_target_percent(context.sym, 0)

Key features:

• Walk-forward optimization: Rolling and expanding walk-forward optimization, widely considered the best technique for validating machine learning models in finance.
• Incremental/out-of-core learning: Train models and run backtests even when your data is too large to fit in memory.
• Multiple machine learning/deep learning packages: Including scikit-learn, Keras + TensorFlow, and XGBoost.

from moonshot import MoonshotML

class DemoMLStrategy(MoonshotML):

    CODE = "demo-ml"
    DB = "demo-stk-1d"

    def prices_to_features(self, prices):
        closes = prices.loc["Close"]
        features = {}
        # use past returns...
        features["returns_1d"]= closes.pct_change()
        # ...to predict next day returns
        targets = closes.pct_change().shift(-1)
        return features, targets

    def predictions_to_signals(self, predictions, prices):
        # buy when the model predicts a positive return
        signals = predictions > 0
        return signals.astype(int)

A hint of what's possible:

• Run third-party backtesters such as backtrader
• Schedule daily downloads of third party data
• Create an options trading script that uses QuantRocket's Python API to query data and place orders using the blotter
• Create and schedule multi-step maintenance tasks such as creating futures calendar spreads based on changing rollover rules

import backtrader as bt

class DualMovingAverageStrategy(bt.SignalStrategy):

    params = (
        ('smavg_window', 100),
        ('lmavg_window', 300),
    )

    def __init__(self):

        # Compute long and short moving averages
        smavg = bt.ind.SMA(period=self.p.smavg_window)
        lmavg = bt.ind.SMA(period=self.p.lmavg_window)

        # Go long when short moving average is above long moving average
        self.signal_add(bt.SIGNAL_LONG, bt.ind.CrossOver(smavg, lmavg))