BTC_RL_Trading_Bot
Unverified ML strategy on Crypto by GioStamoulos. BotFinder score 18 out of 100.
A trading bitcoin agent was created with deep reinforcement learning implementations.
Source: github
BotFinder analysis pending.
BTC_RL_Trading_Bot
BTCRLTRADINGBOT About A trading bitcoin agent was created with deep reinforcement learning implementations. Various experiments were performed on the type of neural network, the type of reinforcement learning algorithm, and the number of daily input values that were initially required by the agent to make the first decision. Also, the agent was made with the assumption that for each day they could "sell" or "buy." Environment & RL Algorithms The agent’s environment (StocksEnv) is an environment that simulates buying and selling situations and can be found at [1]. More specifically, the transactions concern Bitcoin cryptocurrencies, and for the training of the agent, the "Historical Bitcoin Dataset" was utilized, which consists of Bitcoin values from January 2012 to March 2021. The reinforcing learning algorithms used are the Asynchronous Advantage Actor Critic (A2C), the Actor Critic using Kronecker-Factored Trust Region (ACKTR), Proximal Policy Optimization (PPO1), and Trust Region Policy Optimization (TRPO) that were entered through the stable baselines library [2]. Every agent was
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Alerts on changes: coming soon
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Open-source maintainer on GitHub.
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