š Exploring Bayesian Techniques in Algorithmic Trading
Bayesian methods provide a framework in which probabilities are updated as new information becomes available, making them particularly relevant for evolving financial systems.
This project outlines how such techniques were applied to trading-related tasks involving price prediction, parameter adaptation, and risk evaluation.
š Project Highlights
⢠Built two deep learning models to forecast open prices of the Bank Nifty index and its top five constituents
⢠Used 21 months of 1-minute data and 20 years of daily data for training/testing
⢠Integrated techniques such as SMOTE for class imbalance and XGBoost for feature ranking
⢠Applied Keras callbacks (EarlyStopping, ModelCheckpoint) to address overfitting
⢠Focused on consensus-based signals, where both models must agree before executing trades
⢠Managed minute-level and daily datasets using a MySQL database
š§ Tools and Techniques Used
TensorFlow & Keras
Scikit-learn, XGBoost, TA-Lib
SMOTE (Imbalanced-learn)
Feature engineering with RSI, MFI, ATR, Bollinger Bands, and more
Read the full guide with the entire project here:
blog.quantinsti.com/introducā¦
š For readers interested in related topics:
Quant Roles Overview:
quantinsti.com/quant-roles
Quantitative Trader:
quantinsti.com/articles/quanā¦
Quant Analyst & Researcher:
quantinsti.com/articles/quanā¦
Quant Developer:
quantinsti.com/quant-roles/qā¦
Risk Analyst:
quantinsti.com/articles/riskā¦
š Learn more about the EPAT programme:
quantinsti.com/epat
š£ š“ Detailed EPAT Walkthrough & AMA | Live Webinar
Join us for a dedicated session exploring the Executive Programme in Algorithmic Trading (EPAT).
Get a comprehensive overview of the 6-month journey, support offered, alumni success, and post-programme career services. The session concludes with a live Q&A.
šļø Thursday, July 24, 2025
š 8:30 AM EST | 6:30 PM IST | 9:00 PM SGT
š¤ Speaker: Rohan Mathews, Global Business Head ā QuantInsti
š Register:
quantinsti.com/epat
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