GitHub - paperswithbacktest/awesome-systematic-trading: A curated list of awesome libraries, packages, strategies, books, blogs, tutorials f

Awesome Systematic Trading

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We are collecting a list of resources papers, softwares, books, articles for finding, developing, and running systematic trading (quantitative trading) strategies.

What will you find here?

• 136 libraries and packages for research and live trading, with dead and dormant projects flagged

• Strategies from published papers, with the Sharpe ratio each one produced when it was coded and run

• 55 books for beginners and professionals

• 22 videos and interviews

• And also some blogs and courses

What the replication record looks like

We have coded and run 4,843 of these papers over their own full history. Some numbers worth knowing before you pick one to implement:

Half the published record cannot be distinguished from zero on its own sample.

• The median replication returns a Sharpe ratio of 0.37 , and 48% clear a t-statistic of 1.96 .

itself, so a Sharpe of 0.4 needs about 24 of them.

• Median test window: 34 years . A strategy needs roughly (1.96 / Sharpe)² years to prove

information ratio down to 0.21 , so a meaningful slice of the published edge is index exposure rather than skill.

• The median strategy carries a beta of +0.17 to the S&P 500. Removing it takes the median

measurable decay after publication once the market period is controlled for, to within a fifth of a percentage point a year.

• Across 2,838 papers with a record on both sides of their publication date, we could find no

Method and caveats are written up on the wiki .

📈 Interested in trading strategies implemented in Python?

Visit our comprehensive collection at paperswithbacktest.com for exclusive content!

Click here to see the full table of content

• Libraries and packages

• Backtesting and Live Trading

• General - Event Driven Frameworks

• General - Vector Based Frameworks

• Cryptocurrencies

• Trading bots

• Analytics

• Indicators

• Metrics computation

• Optimization

• Pricing

• Risk

• Broker APIs

• Data Sources

• General

• Cryptocurrencies

• Data Science

• Databases

• Graph Computation

• Machine Learning

• TimeSeries Analysis

• Visualization

• Strategies

• Equities

• Bonds

• Commodities

• Currencies

• Cryptocurrencies

• Derivatives

• Multi-asset

• Books

• Beginner

• Biography

• Coding

• Crypto

• General

• High Frequency Trading

• Machine Learning

• Videos

• Blogs

• Courses

How can I help?

You can help by submitting an issue with suggestions and by sharing on Twitter:

Libraries and packages

List of 136 libraries and packages implementing trading bots, backtesters, indicators, pricers, etc. Each library is categorized by its programming language and ordered by descending populatrity (number of stars).

Backtesting and Live Trading

General - Event Driven Frameworks

Repository Description Stars Made with

vnpy Python-based open source quantitative trading system development framework, officially released in January 2015, has grown step by step into a full-featured quantitative trading platform

zipline dormant since 2024-02 Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting.

backtrader dormant since 2024-08 Event driven Python Backtesting library for trading strategies

QUANTAXIS QUANTAXIS 支持任务调度 分布式部署的 股票/期货/期权/港股/虚拟货币 数据/回测/模拟/交易/可视化/多账户 纯本地量化解决方案

QuantConnect Lean Algorithmic Trading Engine by QuantConnect (Python, C#)

Rqalpha A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities

finmarketpy

Python library for backtesting trading strategies & analyzing financial markets (formerly pythalesians)

backtesting.py Backtesting.py is a Python framework for inferring viability of trading strategies on historical (past) data. Improved upon the vision of Backtrader, and by all means surpassingly comparable to other accessible alternatives, Backtesting.

py is lightweight, fast, user-friendly, intuitive, interactive, intelligent and, hopefully, future-proof.

zvt Modular quant framework

WonderTrader WonderTrader——量化研发交易一站式框架

nautilus_trader A high-performance algorithmic trading platform and event-driven backtester

PandoraTrader High-frequency quantitative trading platform based on c++ development, supporting multiple trading APIs and cross-platform

HFTBacktest Highly precise backtest on HFT data in Python+Numba

PyBroker Algorithmic trading in Python with machine learning: rule based and model driven strategies, walkforward analysis and bootstrapped significance tests on the results

Hikyuu C++/Python quantitative research framework built around reusable strategy components, with its own bar and indicator engine

barter-rs Open source Rust framework for building event driven live trading and backtesting systems, running strategies on a near identical engine on both sides

Investing Algorithm Framework Framework for developing, backtesting and deploying automated trading algorithms and trading bots

qf-lib Modular event driven backtester with data vendor and broker integrations, portfolio construction tools and automated PDF reporting

trade-frame C++17 library and sample applications for automated trading of equities, futures, currencies, ETFs and options on IQFeed and Interactive Brokers data

QuantFabric Linux/C++ mid and high frequency trading system for the Chinese futures, stock and bond exchanges

aat An asynchronous, event-driven framework for writing algorithmic trading strategies in python with optional acceleration in C++.

It is designed to be modular and extensible, with support for a wide variety of instruments and strategies, live trading across (and between) multiple exchanges.

sdoosa-algo-trade-python dormant since 2023-09 This project is mainly for newbies into algo trading who are interested in learning to code their own trading algo using python interpreter.

lumibot A very simple yet useful backtesting and sample based live trading framework (a bit slow to run...)

quanttrader dormant since 2024-06 Backtest and live trading in Python. Event based. Similar to backtesting.py.

gobacktest archived A Go implementation of event-driven backtesting framework

PineForge Transpiles PineScript v6 strategies to C++ and runs deterministic offline backtests on user-provided OHLCV data.

FlashFunk High Performance Runtime in Rust

General - Vector Based Frameworks

Repository Description Stars Made with

QTradeX A powerful and flexible Python framework for designing, backtesting, optimizing, and deploying algotrading bots

vectorbt vectorbt takes a novel approach to backtesting: it operates entirely on pandas and NumPy objects, and is accelerated by Numba to analyze any data at speed and scale. This allows for testing of many thousands of strategies in seconds.

pysystemtrade Systematic Trading in python from book Systematic Trading by Rob Carver

bt Flexible backtesting for Python based on Algo and Strategy Tree

ml-quant-trading PyTorch research stack for ML multi-factor trading with 213 factors, bias correction, portfolio optimization, vectorized backtesting, and public validation reports

Cryptocurrencies

Repository Description Stars Made with

Freqtrade Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.

Jesse Jesse is an advanced crypto trading framework which aims to simplify researching and defining trading strategies.

OctoBot Cryptocurrency trading bot for TA, arbitrage and social trading with an advanced web interface

Kelp archived Kelp is a free and open-source trading bot for the Stellar DEX and 100+ centralized exchanges

basana

Python async and event driven framework for algorithmic trading, with a focus on crypto currencies

openlimits dormant since 2022-07 A Rust high performance cryptocurrency trading API with support for multiple exchanges and language wrappers.

bTrader archived Triangle arbitrage trading bot for Binance

crypto-crawler-rs dormant since 2023-03 Crawl orderbook and trade messages from crypto exchanges

Hummingbot A client for crypto market making

cryptotrader-core dormant since 2019-06 Simple to use Crypto Exchange REST API client in rust.

Trading bots

Trading bots and alpha models. Some of them are old and not maintained.

Repository Description Stars Made with

Blackbird no longer available Blackbird Bitcoin Arbitrage: a long/short market-neutral strategy

bitcoin-arbitrage Bitcoin arbitrage - opportunity detector

ThetaGang ThetaGang is an IBKR bot for collecting money

czsc 缠中说禅技术分析工具；缠论；股票；期货；Quant；量化交易

R2 Bitcoin Arbitrager dormant since 2023-04 R2 Bitcoin Arbitrager is an automatic arbitrage trading system powered by Node.js + TypeScript

Intelligent Trading Bot Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering

analyzingalpha dormant since 2023-08 Implementation of simple strategies

PyTrendFollow dormant since 2018-04 PyTrendFollow - systematic futures trading using trend following

TradeSight AI-powered algorithmic trading platform with RSI/MACD signals, overnight strategy tournaments, paper trading via Alpaca, multi-stock scanning, and web dashboard

PRISM-INSIGHT AI-powered stock analysis with 13 specialized agents, automated trading via KIS API (Korean & US markets)

Analytics

Indicators

Libraries of indicators to predict future price movements.

Repository Description Stars Made with

ta-lib Perform technical analysis of financial market data

go-tart dormant since 2021-06 A Go implementation of the [ta-lib](( https://github.com/mrjbq7/ta-lib ) with streaming update support

pandas-ta no longer available Pandas Technical Analysis (Pandas TA) is an easy to use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns

finta archived Common financial technical indicators implemented in Pandas

ta-rust dormant since 2024-07 Technical analysis library for Rust language

kand Technical analysis library written in Rust with Python and WASM bindings, exposing both batch and incremental streaming updates

wickra Streaming-first technical-analysis library with a Rust core and native Python/Node/WASM bindings plus a C ABI (C, C++, C#/.NET, Go, Java, R); 514 O(1)-per-tick indicators across 24 families, bit-exact batch and streaming

Metrics computation

Librairies of financial metrics.

Repository Description Stars Made with

quantstats Portfolio analytics for quants, written in Python

ffn A financial function library for Python

Optimization

Repository Description Stars Made with

skfolio Portfolio optimization built on top of scikit-learn. It provides a unified interface and sklearn compatible tools to build, tune and cross-validate portfolio models.

PyPortfolioOpt Financial portfolio optimizations in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity

Riskfolio-Lib Portfolio Optimization and Quantitative Strategic Asset Allocation in Python

empyrial Empyrial is a Python-based open-source quantitative investment library dedicated to financial institutions and retail investors, officially released in March 2021

cvxportfolio Portfolio optimization and back-testing from the Stanford convex optimization group, implementing the multi-period framework of Boyd et al.

Deepdow dormant since 2024-01

Python package connecting portfolio optimization and deep learning. Its goal is to facilitate research of networks that perform weight allocation in one forward pass.

spectre Portfolio Optimization and Quantitative Strategic Asset Allocation in Python

Pricing

Repository Description Stars Made with

tf-quant-finance High-performance TensorFlow library for quantitative finance from Google

FinancePy A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives

PyQL

Python wrapper of the famous pricing library QuantLib

Risk

Repository Description Stars Made with

pyfolio dormant since 2023-12 Portfolio and risk analytics in Python

Broker APIs

Repository Description Stars Made with

ccxt A JavaScript / Python / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges

Ib_insync archived

Python sync/async framework for Interactive Brokers.

pmxt Unified prediction market trading API across Polymarket, Kalshi and other venues, in the spirit of ccxt

Coinnect dormant since 2021-11 Coinnect is a Rust library aiming to provide a complete access to main crypto currencies exchanges via REST API.

PENDAX dormant since 2024-05 Javascript SDK for Trading, Data, and Websockets for FTX, FTXUS, OKX, Bybit, & More.

Data Sources

General

Repository Description Stars Made with

Fincept Terminal Fincept Terminal is a comprehensive CLI tool that provides financial insights, market analysis, and a host of other financial services such as technical analysis, fundamental analysis, sentiment analysis, quantitative analysis, and economic data services.

OpenBB Terminal Investment Research for Everyone, Anywhere.

TuShare dormant since 2024-03 TuShare is a utility for crawling historical data of China stocks

yfinance yfinance offers a threaded and Pythonic way to download market data from Yahoo!Ⓡ finance.

AkShare AKShare is an elegant and simple financial data interface library for Python, built for human beings!

FinanceDatabase Database of 300,000+ symbols covering equities, ETFs, funds, indices, currencies, cryptocurrencies and money markets

FinanceToolkit 200+ financial metrics, ratios, technical indicators and risk measures computed from Financial Modeling Prep and Yahoo Finance data

pandas-datareader Up to date remote data access for pandas, works for multiple versions of pandas.

edgartools SEC EDGAR filings in Python: XBRL fundamentals, 13F institutional holdings, insider transactions (Forms 3/4/5) and 8-K events

Quandl archived Get millions of financial and economic dataset from hundreds of publishers via a single free API.

findatapy findatapy creates an easy to use Python API to download market data from many sources including Quandl, Bloomberg, Yahoo, Google etc. using a unified high level interface.

Investpy Financial Data Extraction from Investing.com with Python

Fundamental Analysis Data Fully-fledged Fundamental Analysis package capable of collecting 20 years of Company Profiles, Financial Statements, Ratios and Stock Data of 20.000+ companies.

Wallstreet dormant since 2024-07 Wallstreet: Real time Stock and Option tools

pwb-toolbox Loader for the 32 Papers With Backtest datasets on Hugging Face: daily prices back to 1962 for stocks, ETFs, indices, currencies and commodities, sovereign yield curves, quarterly fundamentals, FRED-MD macro series, and 5.

7 billion rows of 1-minute US equity bars. Cards and schemas are open to read, downloads are gated.

Cryptocurrencies

Repository Description Stars Made with

Cryptofeed Cryptocurrency Exchange Websocket Data Feed Handler with Asyncio

Gekko-Datasets dormant since 2018-05 Gekko trading bot dataset dumps. Download and use history files in SQLite format.

CryptoInscriber dormant since 2018-03 A live crypto currency historical trade data blotter. Download live historical trade data from any crypto exchange.

Crypto Lake High frequency order book & trade data for crypto

Data Science

Repository Description Stars Made with

TensorFlow Fundamental algorithms for scientific computing in Python

Pytorch Tensors and Dynamic neural networks in Python with strong GPU acceleration

Keras The most user friendly Deep Learning for humans in Python

Scikit-learn Machine learning in Python

Pandas Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

JAX Composable transformations of Python+NumPy programs: automatic differentiation, vectorization and JIT compilation to GPU/TPU

Numpy The fundamental package for scientific computing with Python

Scipy Fundamental algorithms for scientific computing in Python

PyMC Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara

Cvxpy A Python-embedded modeling language for convex optimization problems.

Databases

Repository Description Stars Made with

DuckDB In-process analytical SQL database that queries Parquet and Arrow files directly, a common backend for research datasets

Marketstore no longer available DataFrame Server for Financial Timeseries Data

Tectonicdb dormant since 2024-01 Tectonicdb is a fast, highly compressed standalone database and streaming protocol for order book ticks.

ArcticDB (Man Group) High performance datastore for time series and tick data

PyStore Fast datastore for Pandas time series data, built on Dask and Parquet

Graph Computation

Repository Description Stars Made with

Ray An open source framework that provides a simple, universal API for building distributed applications.

Dask Parallel computing with task scheduling in Python with a Pandas like API

Incremental (JaneStreet) Incremental is a library that gives you a way of building complex computations that can update efficiently in response to their inputs changing, inspired by the work of Umut Acar et. al. on self-adjusting computations.

Incremental can be useful in a number of applications

csp (Point72) High performance reactive stream processing library written in C++ and Python, where the same graph runs in backtest and in real time

Man MDF dormant since 2016-12 Data-flow programming toolkit for Python

GraphKit dormant since 2023-03 A lightweight Python module for creating and running ordered graphs of computations.

Tributary Streaming reactive and dataflow graphs in Python

Machine Learning

Repository Description Stars Made with

AI Hedge Fund Educational hedge fund simulator where a team of LLM agents modelled on well known investors debates and takes positions

QLib (Microsoft) Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.

With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib.

FinGPT Open source financial large language models, with the fine-tuned weights released on HuggingFace

Machine Learning for Trading Code for Machine Learning for Trading (3rd edition), from data sourcing and alpha factor research to live execution

Qbot AI powered quantitative investment platform covering data collection, strategy research, backtesting and live trading

FinRL FinRL is the first open-source framework to demonstrate the great potential of applying deep reinforcement learning in quantitative finance.

MlFinLab (Hudson & Thames) dormant since 2023-10 MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.

TradingGym dormant since 2024-02 Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.

AlphaGen Generating sets of formulaic alpha factors with reinforcement learning

Stock Trading Bot using Deep Q-Learning dormant since 2023-12 Stock Trading Bot using Deep Q-Learning

TimeSeries Analysis

Repository Description Stars Made with

Facebook Prophet Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

statsmodels

Python module that allows users to explore data, estimate statistical models, and perform statistical tests.

tsfresh Automatic extraction of relevant features from time series.

pmdarima A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

Visualization

Repository Description Stars Made with

Perspective Data visualization and analytics component built for large and streaming datasets, originally open sourced by J.P. Morgan

D-Tale (Man Group) D-Tale is the combination of a Flask back-end and a React front-end to bring you an easy way to view & analyze Pandas data structures.

mplfinance dormant since 2024-08 Financial Markets Data Visualization using Matplotlib

btplotting btplotting provides plotting for backtests, optimization results and live data from backtrader.

Strategies

Every strategy below is a published paper that has been coded and run over its own full history. The table is regenerated from the replication catalogue by scripts/build_strategies_table.py , so the numbers move when the catalogue does.

Showing the 61 strongest of 1,687 replications that clear a t-statistic of 1.96 over at least 10 years, up to 12 per asset class. Sharpe ratios are measured on each strategy's own active window, not on a common calendar, and are gross of trading costs.

Series with an annualised volatility outside 1% to 100% are treated as degenerate and dropped. The t-statistic is shown because a Sharpe ratio without one says very little: half the catalogue does not clear it.

Equities

Strategy Sharpe t-stat Volatility Years tested

A Study Of Differences In Returns Between Large And Small Companies In Europe 1.89 11.4 6.4% 37

The Investment CAPM 1.80 11.0 2.9% 37

Important Characteristics, Weaknesses and Errors in German Equity Data from Thomson 1.68 10.2 6.4% 37

The Role of Beta and Size in the Cross-Section of European Stock Returns 1.63 9.6 5.3% 34

Systematic Abnormal Return Variation and Global Market Inefficiencies 1.57 8.2 9.5% 27

Value and Size Effect: Now You See It, Now You Don’t 1.52 9.3 5.9% 37

Properties of the Most Diversified Portfolio 1.50 9.0 14.3% 37

Understanding Momentum and Reversal? 1.36 8.2 5.1% 36

Fact, Fiction, and the Size Effect 1.33 8.1 2.6% 37

The cross-section of returns in frontier equity markets: Integrated or segmented pricing? 1.32 8.0 3.9% 36

Analytical Solution for Kelly’s Criterion for Multiple Outcomes 1.31 7.9 13.3% 37

End-To-End Large Portfolio Optimization For Variance Minimization With Neural Networks Through Covariance Cleaning 1.28 7.3 15.4% 33

Bonds

Strategy Sharpe t-stat Volatility Years tested

Statistical and Economic Benefits of Whitening Residuals in Bond Yields 0.90 5.0 18.6% 30

Dynamic Risk-Aware Yield Search: A Useful Tool for Fixed Income Investors 0.89 4.4 2.9% 25

Out-performing corporate bonds indices with factor investing 0.85 5.1 12.3% 36

Priced risk in corporate bonds 0.72 3.0 6.0% 17

Sitting Bucks: Stale Pricing in Fixed Income Funds 0.64 3.9 5.7% 37

Frontier and Emerging Government Bond Markets 0.62 3.7 13.7% 36

Regime-based portfolio optimisation: A Hidden Markov Model approach for fixed income portfolios 0.62 3.6 6.0% 33

Price Effects of Sovereign Debt Auctions in the Euro-zone: The Role of the Crisis 0.51 3.1 13.4% 37

Are Bond Returns Predictable with Real-Time Macro Data? 0.49 2.5 5.9% 26

Trading the Term Premium 0.45 2.8 5.0% 37

Banks’ exposure to interest rate risk, their earnings from term transformation, and the dynamics of the term structure 0.44 2.7 7.9% 38

Predictable End-of-Month Treasury Returns 0.43 2.6 3.8% 37

Commodities

Strategy Sharpe t-stat Volatility Years tested

How to Improve Commodity Momentum Using Intra-Market Correlation 0.65 2.8 8.7% 19

Long-Run Reversal in Commodity Returns: Insights from Seven Centuries of Evidence 0.63 3.8 20.7% 37

Rolling vs. Expanding Windows in Mean-Reversion Strategies: Evidence from Gold-Silver and Cross-Asset Validation 0.36 2.2 98.5% 37

Currencies

Strategy Sharpe t-stat Volatility Years tested

Good Carry, Bad Carry 1.74 10.6 4.6% 37

The Time-Varying Systematic Risk of 1.53 9.3 4.1% 36

Lessons from the Evolution of Foreign Exchange Trading Strategies 1.24 7.4 12.4% 36

Optimal Currency Shares In International Reserves The Impact Of The Euro And The Prospects For The Dollar 0.68 2.9 55.3% 19

Cryptocurrencies

Strategy Sharpe t-stat Volatility Years tested

How to Design a Simple Multi-Timeframe Trend Strategy on Bitcoin 3.39 16.2 46.3% 23

‘Know When to Hodl ‘Em, Know When to Fodl ‘Em’: An Investigation of Factor Based Investing in the Cryptocurrency Space 1.53 6.2 9.6% 16

Seasonality, Trend-following, and Mean reversion in Bitcoin 1.11 4.5 49.5% 16

Do Risk Preferences Drive Momentum in Cryptocurrencies? 0.68 4.0 54.9% 34

The Blockchain Risk Parity Line: Moving From The Efficient Frontier To The Final Frontier Of Investments 0.58 3.4 54.1% 34

Price Overreactions in the Cryptocurrency Market 0.53 3.1 32.3% 35

Proof-of-What? Detecting original consensus algorithms in cryptocurrencies with a four-factor model 0.52 2.4 85.9% 22

Cryptocurrency as money: A trading strategy solution 0.47 2.8 15.4% 35

Derivatives

Strategy Sharpe t-stat Volatility Years tested

Media Tone Goes Viral: Global Evidence from the Currency Market 1.06 6.5 1.3% 38

Robust Portfolio Optimization with Value-At-Risk Adjusted Sharpe Ratios 0.92 5.6 17.1% 37

When Factor Timing Makes Sense 0.74 4.5 10.4% 37

Rational Decision-Making Under Uncertainty: Observed Betting Patterns on a Biased Coin 0.60 3.7 3.6% 38

Can Financial Innovation Succeed by Catering to Behavioral Preferences? Evidence from a Callable Options Market 0.55 3.4 17.7% 37

A Theory of Model Sophistication and Operational Risk 0.50 3.1 9.4% 37

Tail-Risk Protection Trading Strategies 0.49 3.0 13.6% 37

Is Media Tone just a Tone? Time-Series and Cross-Sectional Evidence from the Currency Market 0.35 2.1 3.1% 36

The Temporal Pattern of Trading Rule Returns and Central Bank Intervention: Intervention Does Not Generate Technical Trading Rule Profits 0.33 2.0 9.3% 37

Arbitrage in the Foreign Exchange Market: Turning on the Microscope 0.32 2.0 8.4% 37

Multi-asset

Strategy Sharpe t-stat Volatility Years tested

Optimal Annuity Risk Management 1.62 9.9 5.4% 38

Explaining low annuity demand: an optimal portfolio application to Japan 1.60 9.8 4.8% 38

Diverging roads: Theory-based vs. machine learning-implied stock risk premia 1.57 9.5 8.3% 37

The Anomalous Behavior of the S&P Covered Call Closed End Fund 1.36 8.3 18.6% 37

Any role for mean reversion in short term asset 1.30 7.4 8.6% 32

Inconsistent investment and consumption problems 1.26 7.7 2.6% 38

Heuristic Portfolio Rules with Labor Income 1.21 7.4 10.5% 38

Investing for the Long-Run in European Real Estate 1.11 4.5 5.5% 16

Regime-Aware Risk Management in Concentrated Equity Portfolios: Evidence from the Magnificent Seven 1.11 6.4 1.4% 33

Are Heuristics Better than Theory if Market Crashes Are 1.10 6.7 8.4% 37

Risk Parity Portfolios with Risk Factors 1.08 6.3 17.3% 34

A Risk Based Approach to Tactical Asset Allocation 1.06 6.4 5.0% 37

Older QuantConnect implementations of some of these papers are kept in static/strategies .

Books

A comprehensive list of 55 books for quantitative traders.

Beginner

Title Reviews Rating

A Beginner’s Guide to the Stock Market: Everything You Need to Start Making Money Today - Matthew R. Kratter

How to Day Trade for a Living: A Beginner’s Guide to Trading Tools and Tactics, Money Management, Discipline and Trading Psychology - Andrew Aziz

The Little Book of Common Sense Investing: The Only Way to Guarantee Your Fair Share of Stock Market Returns - John C. Bogle

Investing QuickStart Guide: The Simplified Beginner’s Guide to Successfully Navigating the Stock Market, Growing Your Wealth & Creating a Secure Financial Future - Ted D. Snow

Day Trading QuickStart Guide: The Simplified Beginner’s Guide to Winning Trade Plans, Conquering the Markets, and Becoming a Successful Day Trader - Troy Noonan

Introduction To Algo Trading: How Retail Trader

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