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Trading strategies, research, and tutorials that you can clone into your deployment.
Introductory tutorial for Moonshot demonstrating data collection, universe selection, and backtesting of an end-of-day momentum strategy. Uses free sample data.
from quantrocket.codeload import clone
clone('moonshot-intro')quantrocket codeload clone 'moonshot-intro'
Introductory tutorial for Zipline demonstrating data collection, interactive research, and backtesting of a momentum strategy for equities. Uses free sample data.
from quantrocket.codeload import clone
clone('zipline-intro')quantrocket codeload clone 'zipline-intro'
In-depth walkthrough of Pipeline, an API for filtering and performing computations on large universes of securities. The Pipeline API is part of Zipline but can also be used on a standalone basis. Uses free sample data.
from quantrocket.codeload import clone
clone('pipeline-tutorial')quantrocket codeload clone 'pipeline-tutorial'
Introductory tutorial for MoonshotML demonstrating walk-forward optimization of a simple machine learning strategy. Uses free sample data.
from quantrocket.codeload import clone
clone('moonshotml-intro')quantrocket codeload clone 'moonshotml-intro'
Learn quantitative finance with this comprehensive lecture series. Adapted for QuantRocket from the Quantopian Lecture Series. Most lectures use free sample data.
from quantrocket.codeload import clone
clone('quant-finance-lectures')quantrocket codeload clone 'quant-finance-lectures'
Learn how to research fundamental factors using Pipeline, Alphalens, and Sharadar price and fundamental data. Demonstrates factor engineering techniques, exploratory data analysis (EDA), Alphalens tear sheet interpretation, sector neutralization, macro analysis, creating multi-factor scores, and more.
from quantrocket.codeload import clone
clone('fundamental-factors')quantrocket codeload clone 'fundamental-factors'
Pairs trading strategy for Moonshot that includes a research pipeline for identifying and selecting pairs. Tests all possible pairs in a universe for cointegration using the Johansen test, then runs in-sample backtests on all cointegrating pairs, then runs an out-of-sample backtest on the 5 best performing pairs. Calculates daily hedge ratios using the Johansen test and times entries and exits using Bollinger Bands.
from quantrocket.codeload import clone
clone('pairs-pipeline')quantrocket codeload clone 'pairs-pipeline'
Intraday FX strategy that exploits the tendency of currencies to depreciate during local business hours and appreciate during foreign business hours. Uses EUR.USD with hourly data from Interactive Brokers. Runs in Moonshot. Interactive Brokers account required but no QuantRocket subscription required for backtesting.
from quantrocket.codeload import clone
clone('fx-bizday')quantrocket codeload clone 'fx-bizday'
Intraday Zipline strategy for US stocks that sells stocks which gap below their moving average after previously trading above it. Demonstrates live trading with Interactive Brokers or Alpaca.
from quantrocket.codeload import clone
clone('sell-gap')quantrocket codeload clone 'sell-gap'
Introduction to QuantRocket's borrow fees dataset. Uses Alphalens to analyze borrow fees as an alpha source and uses Moonshot and Zipline to demonstrate ways to incorporate borrow fees into long or short strategies.
from quantrocket.codeload import clone
clone('borrow-fees-alpha')quantrocket codeload clone 'borrow-fees-alpha'
Machine learning strategy that trains the model using 'everything and the kitchen sink': fundamentals, technical indicators, returns, price levels, volume and volatility spikes, liquidity, market breadth, and more. Runs in Moonshot. Utilizes data from Sharadar and Interactive Brokers.
from quantrocket.codeload import clone
clone('kitchensink-ml')quantrocket codeload clone 'kitchensink-ml'
Moonshot strategy that shorts stocks that fell 10% or more the previous day. Demonstrates how to run a multi-country backtest to find where an anomaly works best. Uses global equities data from EDI.
from quantrocket.codeload import clone
clone('dead-cat-drop')quantrocket codeload clone 'dead-cat-drop'
Value strategy for US stocks modeled on Alpha Architect's QVAL ETF, using enterprise multiple and Piotroski F-Score to target cheap, high-quality stocks. Utilizes Sharadar fundamental and price data. Runs in Moonshot.
from quantrocket.codeload import clone
clone('qval')quantrocket codeload clone 'qval'
Intraday trading strategy for futures calendar spreads. Uses crude oil futures and 1-minute bid/ask bars from Interactive Brokers with a Bollinger Band mean reversion strategy. Runs in Moonshot. Demonstrates using exchange native spreads for live/paper trading, and non-native spreads for backtesting. Interactive Brokers account required but no QuantRocket subscription required for backtesting.
from quantrocket.codeload import clone
clone('calspread')quantrocket codeload clone 'calspread'
Long-only momentum strategy modeled on Alpha Architect's QMOM ETF, selecting stocks with the smoothest momentum and rebalancing the portfolio before quarter end to capture a window-dressing seasonality effect.
from quantrocket.codeload import clone
clone('qmom')quantrocket codeload clone 'qmom'
Value/Momentum/Trend strategy modeled on Alpha Architect's VMOT ETF. This repository provides the trend strategy and walks through backtesting the value, momentum, and trend strategies in tandem. For the value and momentum strategies, see the qval and qmom repositories.
from quantrocket.codeload import clone
clone('vmot')quantrocket codeload clone 'vmot'
Intraday momentum strategy that buys (sells) leveraged ETFs late in the trading session following a significant intraday gain (loss) and holds until the close. From Ernie Chan's book Algorithmic Trading. Uses 1-minute data from QuantRocket. Runs in Moonshot.
from quantrocket.codeload import clone
clone('trend-day')quantrocket codeload clone 'trend-day'
Intraday momentum strategy that buys (sells) the S&P 500 when the first half hour return and penultimate half hour return are both positive (negative). Uses VIX filter to restrict strategy to high volatility regimes. Uses 1-minute SPY data from QuantRocket and 30-minute VIX data from Interactive Brokers. Runs in Moonshot.
from quantrocket.codeload import clone
clone('first-last')quantrocket codeload clone 'first-last'
Tutorial for importing historical futures data from a third-party data provider and combining it with recent data from Interactive Brokers in a Zipline bundle.
from quantrocket.codeload import clone
clone('futures-import')quantrocket codeload clone 'futures-import'
Overview of 3 sentiment datasets from data provider Brain: Brain Sentiment Indicator (news sentiment), Brain Language Metrics on Company Filings, and Brain Language Metrics on Earnings Call Transcripts.
from quantrocket.codeload import clone
clone('brain-sentiment')quantrocket codeload clone 'brain-sentiment'
Comparative analysis of stock market characteristics for 17 countries, including number of listings, short sale availability, volatility, distribution of sectors, etc.
from quantrocket.codeload import clone
clone('global-market-profiles')quantrocket codeload clone 'global-market-profiles'
Equal-weighted and dollar-volume-weighted benchmark strategies for Moonshot.
from quantrocket.codeload import clone
clone('benchmark')quantrocket codeload clone 'benchmark'
Dual moving average crossover strategy for backtrader
from quantrocket.codeload import clone
clone('backtrader-dma')quantrocket codeload clone 'backtrader-dma'
Send a MessageHi, I'm Brian from QuantRocket. Let me know how I can help. You can use this form, email me at brian@quantrocket.nobots.com, or connect and message me on LinkedIn ↗.