Wealth

A working curriculum on money and economics: mindset, fundamentals, a reading list, and AI-driven quant and biotech investing ideas.

· Updated Sep 19, 2023

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Mindset is crucial aspect when dealing with money and knowledge of it.

I guess it's important for me to get an intro to economics.

update on 22 Sep, 25 I'm currently reading Taleb, and he says it's all fraud, it's not easy to theorize social or economic science and do forecasting

People

Explore -> https://github.com/antontarasenko/awesome-economics

Intro to Economics

Books

  • Common Stocks Uncommon Profits

  • Think and Grow Rich

  • Atomic Habits

  • Money Master the Game

  • 4 hour work week

  • Influence

  • Confessions of an advertising man

  • The lean startup

  • Never split the difference

  • How to win friends and influence people

  • 48 Laws of Power

  • The Personal MBA

  • The Intelligent Invester

  • Reality transurfing

  • Misbehaving

  • The creature of Jekyll island

  • The House of Morgan

  • Devil take the hindmost

Economics

Study of People and their choices

  • Opportunity Cost
  • Incentives
  • Macro and Micro Economics

🧭 Strategic Pathway

We'll work in 3 parallel tracks, which synergize:

Track Focus Purpose
A. Quant + Algo Master markets with code Build scalable, AI-driven trading tools
B. Startup Intelligence Learn how winners emerge early Gain edge in early-stage startup investing
C. Edge Skill Leveraging Learn rare, high-leverage skills Build unfair advantage others can’t copy

🧠 Track A: Quant + Algo (Your Technical Arena)

You're ideal for this because you already do AI. You can beat traditional finance people who don't understand modern ML.

🔧 Steps
  1. Month 0–1: Foundations
    • Learn quantitative finance basics:
    • Simulate basic strategies on:
      • QuantConnect or Backtrader (Python)
  2. Month 2–3: Build Micro-Algos
    • Build small, rule-based strategies:
      • Mean reversion
      • Momentum
      • Volatility breakout
    • Use historical data and backtest on your laptop or Google Colab
  3. Month 4+: AI Edge
    • Integrate:
      • Reinforcement learning (e.g., bandits for order sizing)
      • Deep learning for sentiment/trend analysis
    • Use JAX, your AI stack, for speed
    • Explore competition platforms: Numerai, Kaggle (finance contests)
🧰 Tools
  • Data: Yahoo Finance (free), Tiingo (cheap), Quandl (academic access)
  • Infra: Google Colab, Jupyter, JAX, QuantConnect
  • Paper trading: Alpaca, TradingView

⚙️ JAX Quant Lab

🎯 Goal

Build a high-performance, AI-powered quantitative trading lab using JAX.

📚 Core Skills

  • JAX for fast vectorized computation
  • Backtesting & portfolio simulation
  • Optimization (Bayesian, Evolutionary, etc.)
  • Risk-adjusted metrics (Sharpe, Sortino, Tail ratio)

🧪 Projects

  • Build JAX-based backtesting engine
  • Implement 5 strategies (momentum, mean reversion, breakout, etc.)
  • Develop position sizing optimizer (e.g., Kelly Criterion)
  • Create risk-adjusted performance evaluator

✅ Milestones

🎯 Goal

Exploit frontier biotech investing using AI and scientific insight.

📚 Core Skills

  • Molecular biology, pharma pipelines
  • Biotech market structure (FDA, trials, IP)
  • AI for trend analysis and NLP summarization

🧪 Projects

  • NLP tool to classify high-potential biotech startups
  • Simulated biotech index vs S&P 500
  • Create custom “AI-picked Biotech Portfolio”
  • Study biotech news and earnings

✅ Milestones

📚 Open Science Capitalist

🎯 Goal

Use AI to discover investable trends in scientific papers before commercialization.

📚 Core Skills

  • NLP & Embeddings for paper parsing
  • Trend detection (topic modeling, clustering)
  • Scientific commercialization pipeline

🧪 Projects

  • Scrape arXiv, bioRxiv, medRxiv for frontier science
  • Cluster by field, extract novelty metrics
  • Score and rank promising trends
  • Publish weekly “Open Science Radar”

✅ Milestones