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AI-based Trading Strategies applies deep learning models (LSTM, GRU, CNN-LSTM, Transformer) to market prediction and asset allocation. The models are evaluated with transaction cost sensitivity and cross-validation, using metrics like the Sharpe ratio.
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88
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Fix Stage H OOS confirmatory blocks across all models (callbacks, thresholds, summary printing)
e458e1fView on GitHubUpdate README.md project structure after removing duplicate notebook
7c46ccfView on GitHubUpdate README.md to match current project structure
28dea7dView on GitHubAdd Stacked LSTM notebook (renamed to 1_stacked_lstm.ipynb)
d698347View on GitHubAdd Stacked LSTM trading strategy with OOS backtest results
812f757View on GitHubrefactor: updated GRU and CNN-LSTM notebooks for consistency with ATT-LSTM/Transformer workflow
101a8c6View on GitHubRecommit GRU notebook with cleared outputs for clean GitHub rendering
9c7ff6cView on GitHubAdd GRU-based trading strategy with OOS backtest results
c1431fdView on GitHubClean CNN-LSTM notebook: cleared outputs for GitHub readability
58ce28cView on GitHubAdd Optuna hyperparameter tuning for CNN-LSTM (CV AUC optimization)
97dcdd9View on GitHubUpdate .gitignore to ignore artifacts but keep final OOS equity curves
08e4572View on GitHub