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Here’s a daily 7-hour time table designed to systematically cover all topics required to become a professional quant trader. This schedule balances theory, coding, strategy development, and practical application, while ensuring sustainability and avoiding burnout. Adjust timings based on your personal rhythm!
Daily Time Table (7 Hours)
Format: Time Block → Activity → Focus Area
1. Morning Session: Core Technical Skills (3 Hours)
6:00 AM – 7:00 AM: Mathematics & Statistics
- Study probability, calculus, or stochastic processes.
- Example: Work through problems from “Heard on the Street” or practice time-series analysis.
- Resource: Khan Academy, MIT OpenCourseWare.
7:00 AM – 7:15 AM: ☕ Break
7:15 AM – 8:30 AM: Programming & Algorithms
- Practice Python/R/C++ for quant tasks.
- Example: Build a data scraper using pandas, or optimize a backtesting script.
- Resource: LeetCode, “Python for Finance” by Yves Hilpisch.
8:30 AM – 9:00 AM: 🥪 Breakfast & Mental Reset
2. Mid-Morning Session: Financial Theory (1.5 Hours)
9:00 AM – 10:30 AM: Quantitative Finance & Markets
- Study financial instruments, derivatives pricing, or market microstructure.
- Example: Learn Black-Scholes model, or analyze order book dynamics.
- Resource: “Options, Futures, and Other Derivatives” by John Hull, Investopedia.
3. Afternoon Session: Strategy Development (2 Hours)
10:30 AM – 11:00 AM: ☕ Break
11:00 AM – 12:30 PM: Backtesting & Strategy Coding
- Develop/test a trading strategy (e.g., mean-reversion, arbitrage).
- Example: Backtest a pairs trading strategy on QuantConnect.
- Tools: Backtrader, MetaTrader, or self-coded frameworks.
12:30 PM – 1:00 PM: 🍲 Lunch Break
4. Evening Session: Practical Application & Review (1.5 Hours)
1:00 PM – 2:00 PM: Live Markets & Paper Trading
- Analyze real-time data or execute paper trades.
- Example: Track S&P 500 futures on TradingView, or simulate forex trades.
- Tools: Interactive Brokers, TradingView, Alpha Vantage API.
2:00 PM – 2:30 PM: Review & Journaling
- Document lessons learned, strategy performance, and errors.
- Example: Update a trading journal with today’s backtest results.
Weekly Focus Areas
To ensure holistic learning, rotate topics daily within the time blocks:
- Monday: Stochastic calculus + ML-driven strategies.
- Tuesday: Risk management + crypto markets.
- Wednesday: High-frequency trading (HFT) concepts + C++ optimization.
- Thursday: Portfolio theory + Python libraries (e.g., PyTorch).
- Friday: Behavioral finance + live market analysis.
- Saturday: Open-source contributions or freelance projects.
- Sunday: Rest + review weekly progress.
Key Activities to Include Weekly
- Code at Least One Strategy: Build, backtest, and refine.
- Read Research Papers: Spend 1-2 hours/week on arXiv or SSRN.
- Network: Join 1-2 Reddit/Discord discussions or LinkedIn groups.
- Learn Tools: Dedicate 30 mins/day to master Bloomberg Terminal (free alternatives: Koyfin).
Tools & Resources for Daily Use
- Coding: Jupyter Notebook, VS Code, GitHub.
- Data: Yahoo Finance, Polygon API, FRED Economic Data.
- Simulation: QuantConnect, MetaTrader, Thinkorswim.
- Learning: Coursera (ML/Finance courses), QuantInsti blogs.
Pro Tips for Success
- Pomodoro Technique: Work in 50-minute focused blocks + 10-minute breaks.
- Stay Updated: Spend 15 mins/day reading Bloomberg or The Wall Street Journal.
- Physical Activity: Include a 30-minute walk or workout to boost focus.
Sample Day in Action
| Time | Activity | Topic Covered |
|---|---|---|
| 6:00-7:00 AM | Stochastic calculus problems | Math/Stats |
| 7:15-8:30 AM | Optimize Python backtesting script | Programming |
| 9:00-10:30 AM | Study Black-Scholes model | Derivatives Pricing |
| 11:00-12:30 PM | Backtest a pairs trading strategy | Strategy Development |
| 1:00-2:00 PM | Paper trade Nasdaq futures | Live Market Practice |
| 2:00-2:30 PM | Journaling & strategy review | Performance Analysis |
Final Notes
- Consistency > Intensity: Stick to the schedule even if progress feels slow.
- Adapt: Shift focus to weak areas (e.g., spend more time on C++ if targeting HFT).
- Mindset: Treat losses/errors as learning opportunities.
This structured yet flexible plan ensures you cover all quant trading domains systematically. Adjust based on your goals (e.g., crypto vs. equities) and keep iterating! 📈
