This is a supervised Recurrent Neural Network (RNN) learning project treating stock trading as a classification problem. Given input of a 60 day window of pricing data, choose the best action for maximum profit. This uses my earlier https://github.com/TimRivoli/Stock-Price-Trade-Analyzer project for a trading environment, and its SeriesPrediction module for data preparation and model training. To begin with I use CalculateBestActions to generate the target action values. For each day, given the knowledge a stocks prior day's prices, and constraints of one trade per day, four consecutive trade days, and eight possible trade actions, it calculates the sequence of trades that will produce the highest value in 20 days time. The possible trades are: Aggressive Buy, Target Buy, Market Buy, Hold, Market Sell, Target Sell, Aggressive Sell, and CancelAllOrders. Target Buys and Sells are determined by the moving average price plus or minus 1/4 the 5 day average deviation of price. Aggressive Buys and Sells are determined by 1/2 the 5 day average deviation of price. CalculateBestActions is comp

⚠ No verified equity curve — no track-record source connected.

No score history is stored yet — only the current score is shown.

⚠ No live verification account connected — ask for proof before buying.

Alerts on changes: coming soon

Prop-firm compatibility not provided.

Data-completeness & trust index (not a profitability rating)