deep_trading
Unverified ML strategy on Indices by seaweiqing. BotFinder score 18 out of 100.
This project aims to select a supervised algorithm that can predict stock prices basing on historical data and use the predictor generated to form trading strategies.
Source: github
BotFinder analysis pending.
deep_trading
deeptrading Task: 1. Select a supervised algorithm that can predict stock prices basing on historical data. 2. Accordingly formulate a trading strategy (based on predicted values) in order to generate orders dynamically (on same historical training set for back testing) and observe the gain / loss in overall portfolio. 3. Run the same program for 'real-time trades' 4. Select the combination of Machine learning algorithm and Trading strategy to maximize gain for future orders. Reference: Andrés Arévalo, Jaime Niño, German Hernández, & Sandoval, J. . (2016). High-Frequency Trading Strategy Based on Deep Neural Networks. International Conference on Intelligent Computing. Springer, Cham. Yong, B. X. , Rahim, M. R. A. , & Abdullah, A. S. . (2017). A Stock Market Trading System Using Deep Neural Network. Asian Simulation Conference. Springer, Singapore. Huang, C. Y. (2018). Financial Trading as a Game: A Deep Reinforcement Learning Approach. arXiv preprint arXiv:1807.02787. Islam, S. R. (2018). A Deep Learning Based Illegal Insider-Trading Detection and
⚠ No verified equity curve — no track-record source connected.
Drawdown profile
Data unavailable — contact the owner.
Verification ledger
How the score has moved
Recalculated at each data collection. Transparency means showing the bad weeks too.
No score history is stored yet — only the current score is shown.
Reviews & comments
No reviews collected from the source yet.
⚠ No live verification account connected — ask for proof before buying.
Alerts on changes: coming soon
Prop-firm compatibility not provided.
Open-source maintainer on GitHub.
Data-completeness & trust index (not a profitability rating)