trading_gym
Unverified ML strategy on Multi by StateOfTheArt-quant. BotFinder score 18 out of 100.
a unified environment for supervised learning and reinforcement learning in the context of quantitative trading
Source: github
BotFinder analysis pending.
trading_gym
tradinggym tradinggym is a unified environment for supervised learning and reinforcement learning in the context of quantitative trading. Philosophy tradinggym is designed with the idea that, in the context of quantitative trading, different data format is needed for different research task. For example, cross-sectional data is used for explaining the cross-sectional variation in stock returns, time series data is used for timing strategy development, sequential data is used for sequencial-model, e.g. RNN and it variation algorithm. Besides, supervised learning algorithm and reinforcement learning need different data architecture. The goal of tradinggym is to provide a unified environment for supervised learning and reinforcement learning on top of reinforcement learning concepts framework. The main concepts of RL are the agent and the environment. The environment is the world that the agent lives in and interacts with. At every step of interaction, the agent sees a (possibly partial) observation of the state of the world, and then decides on an action to take. then the agent perceiv
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Alerts on changes: coming soon
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Open-source maintainer on GitHub.
Data-completeness & trust index (not a profitability rating)