Tutorials¶

This series covers everything from zero experience to live deployment, spanning trend following, mean reversion, sector rotation, and multi-factor stock selection.

Reading Format

Starting from Tutorial 00, each tutorial begins with an "Overview" table at the top, so you can quickly assess the goal, estimated time, and prerequisites; the full table of contents and in-depth explanations follow as usual.

First time here? Start with: Tutorial 00 — Environment Setup & First Run / Quantitative Trading Fundamentals (Python 3.10+, pip install ., running examples/03_run_backtest.py, opening HTML reports, quantitative basics). Documentation hub: How-To Overview (user guide, API index, FAQ, report metrics reference).

Directory Structure¶

docs/tutorials/
├── prerequisites/          ← Prerequisite knowledge (read as needed)
│   ├── index.md            ← Prerequisites overview
│   ├── python-basics.md    ← Python syntax, pandas/numpy, virtual environments
│   ├── technical-concepts.md  ← Moving averages, RSI, MACD, Bollinger Bands, ATR, KDJ, etc.
│   └── ashare-knowledge.md ← A-share code rules, T+1, limit up/down, indices, fees
├── 00-getting-started.md   ← Environment setup, first backtest, quant basics (required reading)
├── 01-first-strategy.md    ← Write a dual moving average strategy
├── 02-backtesting.md       ← Interpret backtest reports and metrics
├── 03-optimization.md      ← Parameter tuning, portfolio optimization, attribution analysis
├── 04-live-trading.md      ← Paper trading to live deployment
├── 05-rsi-mean-reversion.md  ← RSI mean reversion strategy
├── 06-sector-rotation.md   ← Sector rotation strategy
├── 07-multi-factor.md      ← Multi-factor stock selection
├── 08-combined-strategy.md ← Combined strategy (All-Weather Alpha)
├── 09-param-optimization.md  ← Strategy parameter optimization and auditing
└── 10-ashare-data-risk.md  ← A-share specific data and risk control

Prerequisites (Read as Needed)¶

If you lack a foundation in any of the following areas, we recommend reading the corresponding prerequisite file before starting Tutorial 00:

File	Content	Who It's For
Python Basics & Environment Setup	Syntax cheat sheet, core pandas/numpy usage, virtual environments	Never written Python before
Technical Analysis Fundamentals	OHLCV, moving averages, RSI, MACD, Bollinger Bands, ATR, KDJ, ADX, support/resistance	Never worked with technical indicators
A-Share Market Fundamentals	Stock codes, T+1 settlement, limit up/down, major indices, ST (Special Treatment) stocks, fees & taxes, fundamental data	No A-share investment experience

→ Full prerequisites index: prerequisites/index.md

First-Day Checklist (Recommended in Order)¶

Install and verify:

# PyPI install (recommended, no repo clone needed)
pip install easyquant-eqlib
# Or install from source (from repo root)
# pip install .
python -c "from eqlib import *; print('eqlib OK')"

Run your first complete report (run from the repo directory):
```
python examples/03_run_backtest.py
```
Open reports/*.html to view metric cards, drawdown curves, and trade records.

For quick offline validation, run the local data example (optional):

python examples/06_local_data.py --download-all
python examples/06_local_data.py

Minimal functional validation (optional):

python examples/01_fetch_data.py
pip install -e ".[dev]"
python -m pytest tests/

Alternative: Web Strategy Studio If you prefer a browser-based interface, try the Web Strategy Studio. No Python environment or repo clone needed — write strategies, run backtests, and view reports directly in your browser. Ideal for users who want to skip environment setup. See Web Studio Documentation.

After completing these steps, continue with Tutorial 01 and Tutorial 02.

Tutorial List¶

Prerequisites (Optional, Read as Needed)¶

File	Summary	Est. Reading
Python Basics & Environment Setup	Variables, functions, core pandas/numpy operations, virtual environments	20 min
Technical Analysis Fundamentals	OHLCV, MA / RSI / MACD / Bollinger Bands / ATR / KDJ / ADX, support & resistance	25 min
A-Share Market Fundamentals	Stock code format, T+1 settlement, limit up/down, common indices, ST stocks, fees & taxes	20 min

Environment & Getting Started¶

#	Tutorial	Topic	Est. Reading
00	Environment & Quant Basics	Environment setup, first backtest, quantitative concepts, strategy components, common errors	25 min
01	Write Your First Strategy	Write a dual moving average strategy, run a backtest	20 min
02	Backtest Validation	Interpret reports, risk metrics, portfolio backtesting	20 min
03	Strategy Optimization & Improvement	Parameter tuning, portfolio optimization, attribution analysis	20 min
04	Paper Trading to Live	Paper trading validation, PTrade/QMT export & deployment	15 min

Strategy-Specific Tutorials (Read by Interest)¶

#	Tutorial	Strategy Type	Core Techniques	Est. Reading
05	RSI Mean Reversion Strategy	Mean reversion	RSI, Bollinger Band double confirmation, stop-loss	20 min
06	Sector Rotation Strategy	Sector rotation	Momentum scoring, equal-weight rebalancing, industry API	20 min
07	Multi-Factor Stock Selection	Factor selection	Z-score normalization, factor combination, IC testing	25 min
08	All-Weather Alpha Combined Strategy	Combined strategy	Multi-factor + sector rotation + RSI/Bollinger/MACD + ATR stop-loss	30 min
09	Strategy Parameter Optimization & Auditing	Parameterization & tools	PARAMS, `optimizer.py`, audit logs, review checklist	20 min
10	A-Share Data & Risk Control	A-share specific data	North-bound capital, margin trading, limit up/down, restricted share unlock, portfolio risk control	25 min

Learning Paths¶

Based on your background and goals, choose the most suitable learning path:

Path A: Zero-Foundation Onboarding (Recommended for Beginners)¶

00 Environment & Quant Basics → 01 First Strategy → 02 Backtest Validation → 03 Strategy Optimization → 04 Live Deployment

Suited for: First-time quantitative traders who want a systematic overview of the entire workflow

Path B: Trend Following Strategy Track¶

01 First Strategy (Dual MA) → 02 Backtest Validation → 03 Strategy Optimization (Stop-Loss/Market Filter) → 06 Sector Rotation

Suited for: Aspiring trend traders focused on momentum and moving average breakouts

Path C: Mean Reversion Strategy Track¶

01 First Strategy → 02 Backtest Validation → 05 RSI Mean Reversion → 03 Strategy Optimization

Suited for: Traders looking to buy low and sell high in range-bound markets, focused on RSI and Bollinger Bands

Path D: Stock Selection & Portfolio Track¶

01 First Strategy → 02 Backtest Validation → 07 Multi-Factor Selection → 06 Sector Rotation → 03 Strategy Optimization

Suited for: Users building multi-stock portfolio strategies, focused on quantitative stock selection

Path E: Fast-Track to Live Trading¶

01 First Strategy → 02 Backtest Validation → 04 Paper Trading to Live

Suited for: Users with some foundation who want to deploy strategies to PTrade/QMT as quickly as possible

Path F: Combined Strategy in Practice (Recommended for Advanced Users)¶

05 RSI Mean Reversion → 06 Sector Rotation → 07 Multi-Factor Selection → 08 Combined Strategy

Suited for: Users who have mastered individual strategies and want to fuse all techniques into a single production-grade strategy

Path G: Parameter Optimization & Auditing (Recommended for Users with Some Foundation)¶

01 First Strategy → 02 Backtest Validation → 03 Strategy Optimization → 09 Parameter Optimization & Auditing

Suited for: Users who want to tune parameters via scripts or custom workflows while maintaining auditable records

Browse by Strategy Type¶

Strategy Type	Tutorial	Related Examples
Trend Following (Dual MA)	Tutorial 01, Tutorial 03	Example 02, Example 03
Mean Reversion (RSI)	Tutorial 05	Example 15
Mean Reversion (Bollinger Bands)	Tutorial 05 Section 8	Example 15
MACD Trend Confirmation	Tutorial 03 Section 3.4	Example 16
Sector Rotation	Tutorial 06	Example 10
Multi-Factor Selection	Tutorial 07	Example 17, Example 09
Combined Strategy (All-Weather Alpha)	Tutorial 08	Example 20
Grid Trading	—	Example 18
Support/Resistance	—	Example 08, Example 19
Portfolio Backtesting	Tutorial 02 Section 8	Example 11
Parameter Optimization & Auditing	Tutorial 09	agent/optimizer.py, agent/strategy_template.py
Paper Trading / Live Trading	Tutorial 04	Example 12
A-Share Specific Data & Risk Control	Tutorial 10	—

Prerequisites¶

Python 3.10+ (see Tutorial 00)
eqlib installed: pip install easyquant-eqlib (PyPI) or pip install . / pip install -e . (source)
Python basics (variables, functions, loops, conditionals) → No foundation? See Python Basics & Environment Setup
Technical indicator basics (moving averages, RSI, etc.) → No foundation? See Technical Analysis Fundamentals
A-share market knowledge (T+1 settlement, limit up/down, stock codes, etc.) → No foundation? See A-Share Market Fundamentals

Troubleshooting & API quick reference: FAQ, API Reference.

Difference Between Tutorials and Examples¶

	Tutorials (docs/tutorials/)	Examples (examples/)
Format	Markdown docs + code snippets	Runnable Python scripts
Goal	Systematically learn concepts and methods	Quick reference and copy-paste-run
Content	Explains "why" and "how"	Shows "what the code looks like"

Recommendation: Use tutorials to learn concepts, then run examples to deepen understanding.

Real-World Case Studies¶

After completing the tutorials, explore these real strategy cases:

All-Weather Alpha Combined Strategy (Complete Production-Grade Case)¶

Tutorial 08: All-Weather Alpha Combined Strategy — Fuses all tutorial strategy techniques into a single complete production-grade combined strategy, including multi-factor stock selection, sector rotation, RSI/Bollinger Bands/MACD/ATR technical signals, support/resistance levels, and lifecycle callbacks, with complete backtesting and paper trading code.

Example 20: All-Weather Alpha Combined Strategy — Complete runnable combined strategy code, including the strategy module, backtest script, and paper trading script.

Support/Resistance Portfolio Strategy (Complete Live Trading Case)¶

Example 19: Support/Resistance Portfolio Strategy — A complete multi-stock portfolio strategy case study, including pre-generated backtest reports (HTML/PNG/Markdown/JSON) that can be opened directly in a browser to review strategy performance, or re-run via backtesting.

Strategy highlights: 8 A-share stocks across different sectors, combining support/resistance + RSI + MACD + ATR stop-loss, total return of +137% over the backtest period (2020-2026).