Search Results for "algorithmic trading python" - Page 4
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Showing 154 open source projects for "algorithmic trading python"
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Moliom Solana Bot
Raydium + Pumpfun sniper bot with user-friendly GUI interface. Free
!This bot is completely Free. The open source code available! This bot was developed by me and a friend. Initially we did it for ourselves, but later decided to give in free use to all people. The bot also has a very convenient and understandable for all interface -
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AnyTrading
The most simple, flexible, and comprehensive OpenAI Gym trading
gym-anytrading is an OpenAI Gym-compatible environment designed for developing and testing reinforcement learning algorithms on trading strategies. It simulates trading environments for financial markets, including stocks and forex. -
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fastquant
Backtest and optimize your ML trading strategies with only 3 lines
fastquant is a Python library designed to simplify quantitative financial analysis and algorithmic trading strategy development. The project focuses on making backtesting accessible by providing a high-level interface that allows users to test investment strategies with only a few lines of code. It integrates historical market data sources and trading frameworks so that users can quickly build experiments without constructing complex data pipelines. -
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Shinkai: Local AI Agents
Shinkai allows you to create advanced AI (local) agents effortlessly
Shinkai is a free, open-source AI platform that lets anyone create powerful AI agents without coding. These agents can collaborate with each other, handle complex tasks, and operate in decentralized crypto environments. Key Features: - No-Code Agent Creation - Build specialized agents (trading bots, sentiment trackers, etc.) with simple descriptions - Multi-Agent Collaboration - Agents work together to solve complex problems - Crypto Integration - Built-in support for decentralized... -
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pytrade
Python functions for trading
Python functions for trading. Fonctions Python pour le trading. -
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TradingGym
Trading backtesting environment for training reinforcement learning
TradingGym is a toolkit (in Python) for creating trading and backtesting environments, especially for reinforcement learning agents, but also for simpler rule-based algorithms. It follows a design inspired by OpenAI Gym, offering various environments, data formats (tick data and OHLC), and tools to simulate trading with costs, position limits, observation windows etc. -
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QuantResearch
Quantitative analysis, strategies and backtests
QuantResearch is a large educational repository dedicated to quantitative finance, algorithmic trading, and financial machine learning research. The project contains numerous notebooks and research materials demonstrating quantitative analysis techniques used in financial markets. These include implementations of factor models, statistical arbitrage strategies, portfolio optimization methods, and reinforcement learning approaches to trading. -
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MONITOR BINANCE LIQUIDATIONS
Real-time monitoring of futures liquidations on the Binance exchange.
✅ Requirements: WINDOWS 10 🆓 FREEWARE 🟢 REAL-TIME MONITORING OF FUTURES LIQUIDATIONS ON THE BINANCE EXCHANGE. 🟢 МОНИТОРИНГ ЛИКВИДАЦИЙ ФЬЮЧЕРСОВ НА БИРЖЕ BINANCE В РЕЖИМЕ РЕАЛЬНОГО ВРЕМЕНИ. ⚫ Use a FILTER to monitor the coins you need. Input currency pairs from one or more into the filter. To monitor several coins, in the filter field input them one by one with a comma separator. FOR EXAMPLE: BTCUSDT,ETHUSDT,CfxUSdt You can enter both uppercase and lowercase... -
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Robin-Stocks API Library
This is a library to use with Robinhood Financial App
This is a library to use with Robinhood Financial App. It currently supports trading crypto-currencies, options, and stocks. In addition, it can be used to get real-time ticker information, assess the performance of your portfolio, and can also get tax documents, total dividends paid, and more. The code is simple to use, easy to understand, and easy to modify. With this library, you can view information on stocks, options, and cryptocurrencies in real-time, create your own robo-investor or... -
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ThinkJulia.jl
Port of the book Think Python to the Julia programming language
ThinkJulia.jl is an open source educational project that adapts Think Python by Allen B. Downey into the Julia programming language, with contributions by Ben Lauwens. It provides a comprehensive introduction to programming and computational thinking using Julia’s modern, high-performance features. The book is structured to gradually teach core concepts such as variables, control flow, functions, recursion, object-oriented programming, and data structures, while offering hands-on exercises... -
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quantitative
Quantized transactions python3
...The README and associated lessons walk the user through implementing algorithms, likely covering data handling, backtesting, and maybe simple trading logic. As an open-source educational resource, it’s designed for Python users interested in automatic trading, algorithmic strategies, and financial data analysis. -
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TradeMaster
TradeMaster is an open-source platform for quantitative trading
TradeMaster is a first-of-its-kind, best-in-class open-source platform for quantitative trading (QT) empowered by reinforcement learning (RL), which covers the full pipeline for the design, implementation, evaluation and deployment of RL-based algorithms. TradeMaster is composed of 6 key modules: 1) multi-modality market data of different financial assets at multiple granularities; 2) whole data preprocessing pipeline; 3) a series of high-fidelity data-driven market simulators for mainstream... -
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The Art of Programming
A collection of practical tips can be found at the bottom of this page
The Art of Programming (Second Edition) is a curated collection of programming problems and solutions originally derived from the Microsoft 100 Interview Questions blog series, later refined into a long-running tutorial and ultimately a published book. Created by July, the series began in 2010 and has since evolved into an in-depth exploration of algorithmic thinking, data structures, and coding interview preparation. The repository brings together 42 classic programming problems from the... -
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FedLab
A flexible Federated Learning Framework based on PyTorch
A Python-based framework for federated learning simulation, emphasizing modularity, communication efficiency, and algorithmic flexibility. Supports both server- and client-side customization for research and development purposes. -
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webotron
Using industrial automation techniques for creating web scraping tools
Industry uses machines that can easily maim or kill their operators and is also used in very adverse environments. In spite of this, production quality must be close to perfect without reliance on operator skill or attentiveness. Control programs must be robust, yet simple enough to be understood and maintained by non programmer skilled trades like electricians . The main programming model is the PLC which implements double buffering and an event loop. The most advanced production model... -
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Haircut_EQ
Fetches the historical Haircut data (%) of Equities and Gold Bonds
Haircut is the amount of margin money deducted while pledging the Equites/ Mutual Funds / Bonds for trading. A haircut value of 13% means that, if a share worth of Rs 100 is pledged for trading, then Rs. 13 will be deducted and Rs. 87 will be given as collateral margin for trading. It is based on previous closing price. This program is used to identify the real value of a share with date. It fetches the historical Haircut data (%) and Price of an equity listed in NSE, India at a... -
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Guided Diffusion
Codebase for Diffusion Models Beat GANS on Image Synthesis
The guided-diffusion repository is centered on diffusion models for image synthesis, with a focus on classifier guidance and improvements over earlier diffusion frameworks. It is derived from OpenAI’s improved-diffusion work, enhanced to include guided generation where a classifier (or other guidance mechanism) can steer sampling toward desired classes or attributes. The code provides model definitions (UNet, diffusion schedules), sampling and training scripts, and utilities for guidance and... -
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Blankly
Easily build, backtest and deploy your algo in just a few lines
Blankly is a live trading engine, backtest runner and development framework wrapped into one powerful open-source package. Models can be instantly backtested, paper traded, sandbox tested and run live by simply changing a single line. We built blankly for every type of quant including training & running ML models in the same environment, cross-exchange/cross-symbol arbitrage, and even long/short positions on stocks (all with built-in WebSockets). Blankly is the first framework to enable... -
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BitcoinExchangeFH
Cryptocurrency exchange market data feed handler
BitcoinExchangeFH is a slim application to record the price depth and trades in various exchanges. You can set it up quickly and record all the exchange data in a few minutes! If the exchange is not supported with the WebSocket API feed, it will automatically fall into using its REST API feed. The subscription section specifies the exchange and instruments to subscribe. After receiving the order book or trade update, each handler is updated. For example, for SQL database handler, it is... -
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Haircut_MF
Fetches the historical Haircut data (%) of Mutual Funds and Bonds
Haircut is the amount of margin money deducted while pledging the Equites/ Mutual Funds / Bonds for trading. A haircut value of 13% means that, if a share worth of Rs 100 is pledged for trading, then Rs. 13 will be deducted and Rs. 87 will be given as collateral margin for trading. It is based on previous closing price (NAV). This program is used to identify the real value of an equity / share / mutual fund / Bonds / SGB with date. This program fetches the historical Haircut data (%)... -
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Machine Learning Financial Laboratory
MlFinLab helps portfolio managers and traders
MlFinLab is a comprehensive Python library designed to support the development of machine learning strategies in quantitative finance and algorithmic trading. The project provides a large collection of tools that implement techniques from academic research on financial machine learning. It covers the full lifecycle of developing data-driven trading strategies, including data preprocessing, feature engineering, labeling techniques, model training, and performance evaluation. ... -
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Algobot
Cryptocurrency trading bot with a graphical user interface
Cryptocurrency trading bot that allows users to create strategies and then backtest, optimize, simulate, or run live bots using them. Telegram integration has been added to support easier and remote trading. Please note that Algobot requires TA-LIB. You can view instructions on how to download TA-LIB. For Windows users, it's best to download the .whl package for your Python install and pip install it. -
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Zipline
Zipline, a Pythonic algorithmic trading library
Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies. Quantopian also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more. -
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MachineLearningStocks
Using python and scikit-learn to make stock predictions
MachineLearningStocks is a Python-based template project that demonstrates how machine learning can be applied to predicting stock market performance. The project provides a structured workflow that collects financial data, processes features, trains predictive models, and evaluates trading strategies. Using libraries such as pandas and scikit-learn, the repository shows how historical financial indicators can be transformed into machine learning features. -
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Eiten
Statistical and Algorithmic Investing Strategies for Everyone
Eiten is an open-source Python project focused on providing statistical and algorithmic trading strategies powered by data analysis and machine learning techniques. It is designed to make quantitative investing more accessible by offering ready-to-use strategies that analyze market behavior, detect patterns, and generate actionable insights. The project includes tools for evaluating stock performance, identifying trends, and applying algorithmic models to financial data, enabling users to experiment with different investment approaches. ...
