Preetam Sharma's repositories
TradingPatternScanner
Trading Pattern Scanner Identifies complex patterns like head and shoulder, wedge and many more.
Pricing-Models
Implementation of option pricing models using Numba that performs better. This entire project has utilized as little libraries as possible, even though certain models have their own Machine Learning Model with assessment and performance.
Forex-Trading
A simple implementation of HFT (High-Frequency Trading) in Python on the concept of DQN for forex market
Fibonacci-Retracement
Machine learning model for finding Retracement,Support and Resistance
Forex-Fibonacci
A mathematical model for Fibonacci Retracement and location entry and exit formulation using ML
HFT-market-making
High Frequency trading and its market making
Evolutionary-HFT
Classification of Buy or Sell in HFT data with ensemble model of LightGBM and Random Forest.
HFT-Arbitrage
Find the arbitrage anomaly and repair it for high-frequency trading
HFT-hedging
This repository hosts an algorithm designed for High-Frequency Trading (HFT), The core aim is to optimize hedging decisions at every time step `t`.
Forex-Recovery
A model created based on LSTM, breakout and economic calendar for predicting the recovery rate in Forex market.
HFT-RL-Stocks
An specific version of RL that is know as Twin Delayed DDPG(TD3) implemented for stocks to train a model and trade on high volume and volatility
Kalman-filters
Kalman filters implementation in Financial models for correlation and Linear regression
DeepAR
Time Series forecast using DeepAR and Q-learning
financial-volatility
A Full-Stack volatility measure web-app using FastAPI, Streamlit and PostgreSQL with Docker support
Optimized_cluster
Optimized cluster in finance and detecting false investment strategies using Unsupervised Learning Methods
put-cv-sem5
PUT - Computer Vision lab with Krzysztof Martyn
Quantitative-Finance
Collection of Mathematical financial models with performance ratio
Sustainable-Finance
Impact analysis between ESG (Environmental, Social, and Governance) and Performance of SPX
Temporal-Fusion-Transformer
Research on combined performance analysis Temporal Fusion Transformer and Q-learning for time series forecasting.