@AmolPagare
Amol Pagare
@AmolPagare
Electrical Engineering student at IIT Bombay, building at the edge of VLSI and Quantum Computing
Electrical Engineering student at IIT Bombay, building at the edge of VLSI and Quantum Computing
Mumbai, India
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IBM_Quantum_Challenge_24
Amol's IBM Quantum Challenge Notebooks
Jupyter Notebook
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Sumer_of_Code_24
This project explores core Reinforcement Learning (RL) algorithms, including Bandits, MDPs, Monte Carlo, TD, and Q-learning, with hands-on implementation. It focuses on building RL agents from scratch and applying them to environments like Mountain Car using Gymnasium.
Jupyter Notebook
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FinSearch_24
This project focuses on implementing and comparing various Option Pricing Models, including Black-Scholes, Binomial, and Monte Carlo Simulations .It explores the mathematical foundations, payoffs, and practical accuracy of these models on real stock data. The goal is to analyze model efficiency
Jupyter Notebook
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SQL_Database_Integration
SQL Query which involves integration of databases using the appropriate joins and the use of functions to extract necessary insights.
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Top Projects
Top Projects
I
We developed our strategy based on exponential moving average (EMA) crossovers and optimized the fast and slow EMA periods to maximize returns through parameter tuning and backtesting.
Stock trading can be overwhelming for beginners because of the complexity involved in analyzing the market, identifying good trade signals, and managing risks. Many existing strategies require deep financial knowledge and advanced coding skills. Our strategy simplifies this by using Exponential Moving Average (EMA) crossovers, which is a well-known and reliable technique in technical analysis. We have optimized the fast and slow moving average parameters to maximize returns and minimize risks. Key Benefits: Beginner-friendly: Any newcomer in stock trading can easily implement our code without advanced knowledge. Profit-driven: The optimized EMA crossover settings are designed to catch strong trends and generate consistent profits. Plug-and-play: Users can start using the strategy immediately with minimal setup, making stock trading more accessible. Adaptable: The strategy can be customized for different stocks and market conditions. In summary, our solution allows beginners to trade confidently using a proven strategy, automating complex analysis and helping them make smarter trading decisions.