forx
gym-anytrading
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forx | gym-anytrading | |
---|---|---|
2 | 2 | |
57 | 2,018 | |
- | - | |
3.9 | 4.2 | |
about 2 years ago | about 2 months ago | |
Python | Python | |
GNU General Public License v3.0 only | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
forx
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forx - a command line tool for checking exchange rates between currencies, both crypto and fiat.
Installation instructions and source code on the github: https://github.com/Gbox4/forx
- [OC] forx - a command line tool for checking exchange rates between currencies, both crypto and fiat.
gym-anytrading
What are some alternatives?
trading-server - Multi-asset, multi-strategy, event-driven trading platform for running low to medium freq strategies at many venues simultaneously with portfolio-based risk management and %-per-strategy capital allocation. Supports event-driven backtesting across all desired instruments, venues and strategies under a single parameterized portfolio.
FinRL - FinRL: Financial Reinforcement Learning. π₯
uniswap-python - π¦ The unofficial Python client for the Uniswap exchange.
backtesting.py - :mag_right: :chart_with_upwards_trend: :snake: :moneybag: Backtest trading strategies in Python.
Auto-GPT-MetaTrader-Plugin - The AutoGPT MetaTrader Plugin is a software tool that enables traders to connect their MetaTrader 4 or 5 trading account to Auto-GPT.
Trading212API - An unofficial API for Trading212
Wizardry - π« Wizardry is an open-source CLI for building powerful algorithmic trading strategies δΊ€ζζ‘ζΆ
gym-mtsim - A general-purpose, flexible, and easy-to-use simulator alongside an OpenAI Gym trading environment for MetaTrader 5 trading platform (Approved by OpenAI Gym)
Agar.io_Q-Learning_AI - An experiment on the performance of homemade Q-learning AIs in Agar.io depending on their state representation and available actions
MO-Gymnasium - Multi-objective Gymnasium environments for reinforcement learning