CS to Systematic Trading

Apply your computer science and machine learning background to systematic trading. Build alpha signals, backtesting pipelines, and production trading systems that compound capital.

6-9 Months Duration
16 Chapters Core Content
Systems Focus Orientation

Stack Translation

SOFTWARE ENGINEERING
Your Current Stack
DB
Data Layer
PostgreSQL, Redis, Kafka
ML
ML Pipeline
Scikit-learn, PyTorch, feature eng
API
Services
REST, gRPC, microservices
CI
DevOps
Docker, K8s, monitoring
SYSTEMATIC TRADING
Target Stack
MKT
Market Data
Tick data, order books, news feeds
α
Alpha Research
Signal generation, backtesting
EXE
Execution
FIX, order routing, algos
RSK
Risk Systems
Real-time P&L, limits, alerts

What You'll Build

alpha_signal.py
class MomentumSignal(AlphaBase): """Cross-sectional momentum with volatility scaling.""" def compute(self, universe: pd.DataFrame) -> pd.Series: # 12-month momentum, skip last month returns = universe['adj_close'].pct_change(252).shift(21) # Volatility scaling (inverse vol weighting) vol = universe['adj_close'].pct_change().rolling(63).std() scaled = returns / vol # Cross-sectional z-score z_score = (scaled - scaled.mean()) / scaled.std() return z_score.clip(-3, 3) # Winsorize outliers

Curriculum Structure

WEEKS 1-4
Market Data Architecture
Understand market data sources, normalization, and storage. Build pipelines that handle tick data, corporate actions, and survivorship bias. Apply your data engineering skills.
Vol I Ch. 1-3 Data Infra
WEEKS 5-10
Alpha Research Framework
Design and backtest trading signals. Handle look-ahead bias, turnover constraints, and transaction costs. Build reusable research infrastructure.
Vol I Ch. 4 Vol I Ch. 8-9 Backtesting
WEEKS 11-16
Portfolio Construction
Combine signals into portfolios. Optimization under constraints, risk budgeting, rebalancing. Integrate with your alpha layer.
Vol I Ch. 10-12 Optimization
WEEKS 17-24
Execution Systems
Market microstructure and algorithmic execution. Build TWAP, VWAP, and implementation shortfall algorithms. Minimize market impact.
Vol II Ch. 23 Vol II Ch. 24
WEEKS 25-32
Risk Infrastructure
Real-time risk monitoring, position limits, and alerting. VaR, stress testing, and drawdown control. Production-grade risk systems.
Vol II Ch. 22 Risk Eng
WEEKS 33-36
Production Deployment
Paper trading to live trading. Monitoring, alerting, and circuit breakers. Handle failures gracefully. Ship your first strategy.
Vol II Ch. 27 DevOps

Career Outcomes

Quantitative Researcher
Design and backtest trading signals at systematic funds. Apply ML to alpha generation. High demand at Renaissance, Two Sigma, Citadel.
Quantitative Developer
Build trading infrastructure, execution systems, and research platforms. Software engineering applied to finance. Strong compensation.
Algorithmic Trader
Manage systematic strategies with real capital. Combine technical and business skills. Path to running your own book or fund.

Ship Alpha, Not Features

Your engineering skills are directly applicable. Learn to build systems that compound capital instead of user metrics.

Begin Path Execution Lab