Financial market forecasting using deep reinforcement learning

@citijayamala

1. I am an expert in Statistics, Regression analysis, Linear regression analysis, p value, ANOVAs, etc. I use excel and other statistical software like SPSS, STATA, E-views for data analysis and statistical analysis based on client requirement. 2. Have done many projects in statistics using SPSS, STATA, E-views. I read your project and sure I can handle your project. 3. Your project will be delivered on time with high standard 4. Assistance will be provided with number of clarifications until client satisfaction 5. I will provide assistance even after the payment. And will maintain data (content) security.

@devanshkhandekar

Hi ,I’m a seasoned Applied Data Scientist with more than 6 YOE & with a finance/audit/security background & hands-on experience building reproducible market analytics pipelines: clean data ingestion, leakage-safe backtesting, rigorous benchmarking & defensible reporting. I’ve delivered research-style packages (code + models + writeups) important for academic review and “auditability” of experiments. Relevant experience * Built time-series forecasting & risk analytics workflows (returns/volatility regimes, rolling backtests, walk-forward validation) with strict controls to avoid lookahead bias & overfitting * Implemented trading-strategy evaluation with realistic assumptions (transaction costs, slippage), performance metrics (CAGR, Sharpe, max drawdown)& clear equity-curve diagnostics * Developed ML pipelines with strong reproducibility: fixed seeds, versioned configs, saved checkpoints/weights What I will deliver • E2E Python project for gold & silver using a justified DRL algorithm (PPO/A2C/DQN via Stable-Baselines) trained only on historical data • Baselines for credibility: naïve/ARIMA or simple supervised model + classic volatility model (GARCH/rolling) for comparison. • Clean backtest on a hold-out period with plots: equity curve, volatility forecasts, training curves & ablations. • Full code/notebooks, trained weights & a quick run guide.

@growwithgdp

✅ Design DRL framework (PPO/A2C) in Python + PyTorch/Stable-Baselines3, justified vs ARIMA/LSTM baseline ✅ Historical gold & silver data preprocessing, feature engineering, volatility modeling & backtesting ✅ Performance metrics, equity curves, volatility plots & reproducible notebooks ✅ Clean code, trained weights & quick-start GPU reproduction guide

@suvrag9

Hello I m a suvra and I am logo designer . If you interested to my work please contact me. . I recently completed few works. . I’d love to apply that same strategic thinking to your project.