TildAlice – Tech Blog for Developers
Python, AI, Deep Learning, Data Analysis, Computer Vision, Reinforcement Learning โ tips and tutorials
Latest Posts
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Reward Engineering for Financial and Robotic RL Tasks
Reward shaping doubled our RL agent's success rate. Compare dense vs sparse rewards, risk-adjusted trading, and curriculum learning with benchmarks.
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Technical Indicators in Practice: MA, RSI, and MACD
Backtested RSI, MACD, and moving averages on 5 years of data. Results show which signals actually work and which are noise. Code included.
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Stable Baselines3: Tips for Training Robust RL Agents
Train production-ready RL agents with Stable Baselines3: hyperparameter tuning tricks, TensorBoard monitoring, and debugging NaN rewards fast.
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Part 3: Policy Gradient vs. Q-Learning: Choosing the Right Agent
DQN vs PPO vs SAC: when each algorithm wins. Math foundations, code examples, and a decision tree for picking the right RL agent for your task.
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Building Custom Gym Environments with OpenAI Gymnasium
Build a custom stock trading Gym environment from scratch: state design, reward engineering, and vectorization. Working code ready to train agents.
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Part 1: The Core of RL: Markov Decision Processes (MDP) Explained
Master MDPs and Bellman equations: the math foundation of RL explained with Python code. Understand value functions and dynamic programming clearly.
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yfinance vs pandas-datareader: Python Stock Data in 2026
yfinance failed to get ticker? pandas-datareader returning errors? We benchmark speed, reliability, and data quality โ one library was 3x faster for stock price analysis in Python.
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Part 5: Automating Earnings Call Summarization with LLMs
Build an SEC filing summarizer with LLMs: EDGAR API, anti-hallucination guards, and structured extraction. Full pipeline with code.