Machine Learning and AI
Backtest and live trade machine learning and deep learning trading strategies with QuantRocket
Walk-forward optimization
Support for 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
Support for multiple Python machine learning packages including scikit-learn, Keras + TensorFlow, and XGBoost.A simple ML strategy
from moonshot import MoonshotML
class DemoMLStrategy(MoonshotML):
CODE = "demo-ml"
DB = "demo-stk-1d"
def prices_to_features(self, prices):
closes = prices.loc["Close"]
# create a dict of DataFrame features
features = {}
# use past returns...
features["returns_1d"]= closes.pct_change()
features["returns_2d"] = (closes - closes.shift(2)) / closes.shift(2)
# ...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)Run it!
>>> from quantrocket.moonshot import ml_walkforward
>>> ml_walkforward("demo-ml",
start_date="2006-01-01", end_date="2012-12-31",
train="Y", min_train="4Y",
filepath_or_buffer="demo_ml*")Machine learning support is built on top of Moonshot, the backtester for data scientists.
Pandas-based: Moonshot is based on Pandas, the centerpiece of the Python data science stack. If you love Pandas you'll love Moonshot. Moonshot can be thought of as a set of conventions for organizing Pandas code for the purpose of running backtests.
Lightweight: Moonshot is simple and lightweight because it relies on the power and flexibility of Pandas and doesn't attempt to re-create functionality that Pandas can already do. No bloated codebase full of countless indicators and models to import and learn. Most of Moonshot's code is contained in a single Moonshot class.
Fast: Moonshot is fast because Pandas is fast. No event-driven backtester can match Moonshot's speed. Speed promotes alpha discovery by facilitating rapid experimentation and research iteration.
Multi-asset class, multi-time frame: Moonshot supports end-of-day and intraday strategies using equities, futures, and FX.
Live trading: Live trading with Moonshot can be thought of as running a backtest on up-to-date historical data and generating a batch of orders based on the latest signals produced by the backtest.
No black boxes, no magic: Moonshot provides many conveniences to make backtesting easier, but it eschews hidden behaviors and complex, under-the-hood simulation rules that are hard to understand or audit. What you see is what you get.
Send a Message