In this groundbreaking video, I reveal one of the most sophisticated, end-to-end trading workflows I’ve ever developed—100% generated by AI. Witness the entire process, from a quant research PDF to a fully functional C++ high-frequency trading (HFT) bot simulator for Micro E-mini (MEES) futures and options.
This is the closest a retail trader can get to true institutional quant and HFT methodologies. I spent days refining the AI prompting to create this seamless pipeline that takes trading ideas, backtests them, forecasts their potential, and generates production-ready C++ code. If you’re serious about algorithmic trading, you don’t want to miss this.
📈 THE COMPLETE AI-DRIVEN WORKFLOW YOU’LL SEE:
THE BLUEPRINT (The Quant PDF): We start with an AI-generated PDF filled with sophisticated quant formulas and strategies for futures and options, including order flow mechanics and arbitrage.
THE BACKTEST (Python & Streamlit): I feed two years of real MEES market data into a custom Python Streamlit dashboard. The AI-generated code backtests numerous strategies (LDI Momentum, Long Straddle, Iron Condor) to identify the top performers based on Sharpe Ratio, Volatility, and Win Rate.
THE FORECAST (Walk-Forward Analysis): Using a second Streamlit dashboard, we forecast the future performance of the winning strategies. This involves advanced modeling with Geometric Brownian Motion (GBM), GARCH, and Jump Diffusion to project returns and volatility.
THE ENGINE (C++ HFT Simulators): The final, most powerful step. I showcase three separate C++ simulators, each built by AI to trade based on the top models (GBM, GARCH, Jump Diffusion). This code is clean, efficient (using only the standard library), and designed to be the core logic for a live trading bot connected to the Rhythmic API.
🕒 TIMESTAMPS:
0:00 - Introduction to the Sophisticated End-to-End Workflow
1:08 - Step 1: The AI-Generated Quant Research PDF
4:17 - The Real Market Data for MEES (Micro E-mini S&P)
5:10 - Step 2: The Python Backtesting Dashboard in Streamlit
9:53 - Analyzing Futures vs. Options Strategies
10:30 - Identifying the Top Performing Strategies (Iron Condor & Long Straddle)
14:35 - Deep Dive: Performance Metrics of the Iron Condor Strategy
18:52 - Step 3: The Python Forecasting Dashboard
20:23 - Forecasting with GARCH, GBM, and Jump Diffusion Models
24:53 - Step 4: The C++ Trading Bot Simulators
25:51 - C++ Simulator 1: Geometric Brownian Motion (GBM) Model
30:09 - C++ Simulator 2: GARCH Model for Adaptive Volatility Trading
35:43 - C++ Simulator 3: Merton Jump Diffusion Model for HFT
39:16 - Final Thoughts & The Path to Live Trading
40:31 - How to Get the Full Source Code & Learn More
DISCLAIMER:
The content in this video is for educational and informational purposes only. It is not financial or investment advice. Trading involves significant risk, and you should consult with a qualified professional before making any financial decisions. The performance results shown are based on backtests and simulations and are not a guarantee of future results.
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