Personal Quant Trading System

A realistic, modular quantitative trading system architecture for solo developers.

Philosophy

1. Discipline > intelligence
2. Risk control > prediction
3. Simplicity > complexity
4. Capital preservation > high returns

You don't need HFT. You don't need deep learning. You need consistency.

Architecture

Six layers: Data → Research → Signal → Portfolio → Execution → Monitoring

┌─────────────────────────────────────────────────────────────────┐
│                         MONITORING                               │
│                    (Dashboard, Alerts)                           │
├─────────────────────────────────────────────────────────────────┤
│                         EXECUTION                                │
│                  (Order Generation, Broker API)                  │
├─────────────────────────────────────────────────────────────────┤
│                         PORTFOLIO                                │
│              (Position Sizing, Risk Controls)                    │
├─────────────────────────────────────────────────────────────────┤
│                          SIGNAL                                  │
│                (Alpha Engine, Factor Models)                     │
├─────────────────────────────────────────────────────────────────┤
│                         RESEARCH                                 │
│              (Backtesting, Walk-Forward Validation)              │
├─────────────────────────────────────────────────────────────────┤
│                           DATA                                   │
│                (Raw, Processed, Features)                        │
└─────────────────────────────────────────────────────────────────┘

Layers

1. Data Layer (Foundation)

Goal: Clean, structured, reliable historical + live data.

Data Types:

• OHLCV price data
• Fundamentals (optional)
• ETF holdings (optional)
• News sentiment (optional, later stage)

Sources (Retail-Friendly):

• Broker API (e.g., Interactive Brokers)
• Yahoo Finance (research only)
• Polygon / Alpha Vantage (paid APIs)

Storage:

• PostgreSQL (structured)
• Parquet files (fast backtesting)
• Redis (live signals cache)

Structure:

data/
├── raw/
├── processed/
├── features/

⚠️ Rule: Never mix raw and processed data.

---

2. Research Layer (Backtesting Engine)

Goal: Test ideas properly without lying to yourself.

Tools:

• Python
• Pandas / NumPy
• vectorbt (fast research)
• backtrader (event-driven)

Key Components:

✔ Feature Engineering

• Moving averages
• Volatility
• Momentum
• Factor scoring

✔ Signal Logic

if momentum > threshold and volatility < limit:
    signal = 1
else:
    signal = 0

✔ Walk-Forward Validation

• Train: 2015–2020
• Validate: 2021–2022
• Test: 2023–2024

⚠️ Never optimize on full history.

---

3. Signal Layer (Alpha Engine)

This is your "brain". Two realistic options:

Option A: Multi-Factor Model (Recommended)

Score = 0.4 * Momentum + 0.3 * Value + 0.3 * Quality

• Rank top 20% → long
• Bottom 20% → optional short

✔ Simple ✔ Robust ✔ Used by real funds

Option B: Machine Learning Model

• LightGBM
• XGBoost
• Random Forest

Predict:

• Next 5-day return
• Probability of positive return

⚠️ Do NOT predict exact price.

if probability > 0.6:
    buy

---

4. Portfolio Construction Layer

Most beginners ignore this. Big mistake.

Equal Weight (Simple Start):

Position Size = Capital / number_of_positions

Volatility Targeting (Better):

Position Size ∝ 1 / Volatility

Risk Controls:

• Max 5% per position
• Max 20% per sector
• Stop-loss (optional)
• Max portfolio drawdown rule

This matters more than prediction accuracy.

---

5. Execution Layer

Keep it simple.

Signal → Order Generator → Broker API → Execution → Confirmation

Rules:

• Trade once per day (avoid overtrading)
• Use limit orders
• Avoid market open chaos
• Log everything

---

6. Monitoring & Risk Dashboard

Track:

• Daily PnL
• Drawdown
• Sharpe ratio
• Win rate
• Exposure
• Turnover

Simple solution:

• Streamlit dashboard
• Or basic web UI

---

Risk Management (Non-Negotiable)

Hard rules:

• Stop trading if drawdown > 20%
• Kill switch
• Capital allocation limit
• No leverage initially

Professionals survive because of risk control.

---

Optional: AI Agent Layer (2026)

Add an LLM agent to:

• Analyze news sentiment
• Summarize earnings
• Generate factor ideas
• Monitor anomalies

But: LLM ≠ price predictor. It's an assistant, not the alpha.

---

Tech Stack

Layer	Tech
Language	Python
Storage	PostgreSQL + Parquet
ML	LightGBM
Backtest	vectorbt
Broker	Interactive Brokers
Dashboard	Streamlit
Deployment	VPS / AWS

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Minimal Viable System (3-6 months)

If you want something you can actually build:

1. US ETFs only
2. Monthly rebalance
3. Multi-factor ranking
4. Equal weight
5. Max 10 positions
6. Manual execution first

---

Status

🚧 Early concept — architecture captured, implementation TBD.