Preetam Sharma's Projects
Implementation of Black Scholes model in python and tested on Options
Family Budget for sharing expenses and income
Volatility measure and visulization in quantative finance
Time Series forecast using DeepAR and Q-learning
Classification of Buy or Sell in HFT data with ensemble model of LightGBM and Random Forest.
Machine learning model for finding Retracement,Support and Resistance
A Full-Stack volatility measure web-app using FastAPI, Streamlit and PostgreSQL with Docker support
Real-time economic events analysis and bi directional forecast
A mathematical model for Fibonacci Retracement and location entry and exit formulation using ML
A model created based on LSTM, breakout and economic calendar for predicting the recovery rate in Forex market.
A simple implementation of HFT (High-Frequency Trading) in Python on the concept of DQN for forex market
A Deep Reinforcement Learning model for high volume and frequency Forex Portfolio Management
Find the arbitrage anomaly and repair it for high-frequency trading
This repository hosts an algorithm designed for High-Frequency Trading (HFT), The core aim is to optimize hedging decisions at every time step `t`.
High Frequency trading and its market making
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 implementation in Financial models for correlation and Linear regression
Optimized cluster in finance and detecting false investment strategies using Unsupervised Learning Methods
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.
PUT - Computer Vision lab with Krzysztof Martyn
Collection of Mathematical financial models with performance ratio