The Building Blocks of Quantitative Trading

1. Why Data Is the Lifeblood of Quant

Trading

Quantitative trading = rules + data + execution.

Without reliable data, even the best models collapse.

Garbage data → garbage results.

• ??Rules without data → useless theory.
• ??Data without rules → noise.
• ??Execution without the first two - gambling.

That's why every quant begins with markets, instruments, and data. This article explores what to trade, what data matters, and how to build your first Datasets.

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2. The Financial Markets Landscape

Quant traders operate across multiple asset classes:

• ??Equities (Stocks) → e.g., Apple (AAPL), Tesla (TSLA).
• ??Fixed Income (Bonds) → U.S. Treasuries, corporate credit.
• ??Foreign Exchange (FX) → EUR/USD, USD/JPY, GBP/USD.
• ??Commodities → Gold, oil, agricultural futures.
• ??Derivatives → Options, futures, swaps.
• ??Crypto DeFi → Bitcoin (BTC), Ethereum (ETH), perpetual futures.

Each has unique behaviors:

• ??FX = highly liquid, trades 24/5.
• ??Equities = restricted hours, more regulated.
• ??Crypto = trades 24/7, highly volatile.
• e, Alpha Vantage, Quandl, FRED.
• ??Paid → Bloomberg, Refinitiv, FactSet.
• ??Hybrid → Polygon.io, Tiingo, Intrinio. ??
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3. Instruments: What Quants Trade

1. ???Stocks (Equities)
2. ???Bonds
3. ???Futures
4. ???Options
5. ???Foreign Exchange (FX)
6. ???Crypto

4. Types of Market Data

1. ???Price Data (OHLCV)
2. ???Fundamental Data
3. ???News Sentiment Data
4. ???Alternative Data
5. ???Free vs Paid Data

• ??Free → Yahoo Finance, Alpha Vantage, Quandl, FRED.
• ??Paid → Bloomberg, Refinitiv, FactSet.
• ??Hybrid → Polygon.io, Tiingo, Intrinio.

? For beginners → free is fine, but note: poor quality = weak backtests.
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8. How Quants Think About Data Quality

• ??Completeness → no missing days.
• ??Accuracy → cross-verify with multiple feeds.
• ??Latency → critical for intraday/HFT.
• ??Survivorship Bias → avoid datasets missing delisted firms.
• ??Look-Ahead Bias → prevent future data leaking into past backtests.

9. Common Beginner Mistakes

1. ???Blindly trusting Yahoo Finance.
2. ???Ignoring dividends/splits.
3. ???Forgetting about bid/ask spread slippage.
4. ???Not cleaning NaNs or price spikes.

10. Case Study: Tesla Earnings Shock

• ??Tesla earnings → 3-5% move in one day.
• ??Strategy: straddle (buy both call + put).
• ??Shows how event data + market data → quant edges

11. What's Next?

Now you know:

• ??Where quant data comes from.
• ??How different instruments behave.
• ??How to compare volatility distributions.

* Next article: Python for Finance 101 - Pandas, Numpy, and time series basics.

? Key Takeaways

• ??Quants live die by data quality.
• ??Instruments behave differently (stocks vs crypto).
• ??Python lets you access free data APls quickly.
• ??Always check for bias slippage before backtesting.

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