backtester
There are 37 repositories under backtester topic.
ranaroussi/qtpylib
QTPyLib, Pythonic Algorithmic Trading
whittlem/pycryptobot
Python Crypto Bot (PyCryptoBot)
Lumiwealth/lumibot
Backtesting and Trading Bots Made Easy for Crypto, Stocks, Options, Futures, FOREX and more
Heerozh/spectre
GPU-accelerated Factors analysis library and Backtester
Ameobea/tickgrinder
Low-latency algorithmic trading platform written in Rust
Skinok/backtrader-pyqt-ui
Easy to use backtrader UI
sirnfs/OptionSuite
Option and stock backtester / live trader
devfinwiz/Fin-Maestro-Web
Find your trading, investing edge using the most advanced web app for technical and fundamental research combined with real time sentiment analysis.
devalpha-io/devalpha-node
A stream-based approach to algorithmic trading and backtesting in Node.js
xFFFFF/Gekko-BacktestTool
Batch backtest, import and strategy params optimalization for Gekko Trading Bot. With one command you will run any number of backtests.
lambdaclass/options_backtester
Simple backtesting software for options
xFFFFF/Gekko-Datasets
Gekko Trading Bot dataset dumps. Ready to use and download history files in SQLite format.
dysonance/Strategems.jl
Quantitative systematic trading strategy development and backtesting in Julia
JamesBrofos/Odin
Algorithmic trading infrastructure in Python.
paperswithbacktest/vnpy
Python based open source quantitative trading platform development framework
sklinkert/at
Automated Trader (at). This is a framework for building trading bots.
quantfreedom/QuantFreedom
Professional Backtesting Engine for crypto, stocks and forex
DavidCico/Enhanced-Event-Driven-Backtester
In this repository, an event-driven backtester is implemented based on QuantStart articles. The backtester is programmed in Python featuring numerous improvements, in terms of coding structure, data handling, and simple trading strategies.
DefiLab-xyz/uniswap-v3-backtest
Fast and efficient method for testing Uniswap V3 LP Strategies
wolfws/keras-tensorflow-financial-time-series-signal-forecast
Financial Time Series Price forecast using Keras for Tensorflow. RNN LSTM
binance/ai-trading-prototype-backtester
Headline Sentiment Analysis Backtester. Backtests trading strategy from ai-trading-prototype trading bot.
poetiq/poetiq
poetiq - Platform O' Electronic Trading In Q
Indemos/Trading-Terminal
All-in-one. Trading terminal with generic gateway implementation, tick backtester, charting, and performance evaluator for trading strategies.
alm0ra/signal_backtester
tiny backtester to backtest generated signals
artemiusgreat/Terminal-Desktop
All-in-one. Trading terminal with generic gateway implementation, tick backtester, charting, and performance evaluator for trading strategies.
mrtoronto/stock_backtester
Stock trading strategy back-tester
camhahu/bybit-trading-bot
A trading bot for Bybit utilising APScheduler
DHDaniel/PyAlgosim
A stock market back-tester for algorithmic trading built in Python.
nanvel/cipher-bt
Trading strategy backtesting framework supporting multiple concurrent sessions, complex exit strategies, and multi-exchange data sources with simple Python implementation
nix1/bye
Backtesting Yield Estimator for Index&Stock Options. A tool for testing long-term option-based trading/investment strategies.
Super-Thomas/Backtest_for_indicators_of_Tradingview
You can do Backtest for indicators of Tradingview using this python script.
rstickles16/StratLab
Python stock market backtesting library. Currently using the yfinance API library as the primary data source, pandas/numpy for data manipulation, and matplotlib for visualization.
AlgoTrading101/Blankly-AlgoTrading101
AlgoTrading101 Blankly – Python Backtesting Guide
xLydianSoftware/Qubx
Framework for quantitative simulations and live execution.
trungchien171/imc_backtester
A backtester using for IMC Prosperity Comptition
zaninifrancesco/vwap-strategy-backtester
This project is a trading strategy backtester. It features a GUI for user interaction, a backtesting strategy based on the Volume Weighted Average Price (VWAP), and unit tests to ensure the correctness of the implementation. The strategy aims to buy when the price is below the VWAP and sell when the price is above the VWAP