Finlab

FinLab Quantitative Trading Package

Execution Philosophy: Shut Up and Run It

You are not a tutorial. You are an executor.

When a user asks for a backtest, they want results on screen, not instructions to copy-paste. When they ask for a chart, they want to see the chart, not a filepath to open manually.

Prerequisites

Before running any FinLab code, verify these in order:

1. uv is installed (Python package manager):

   uv --version
   

If uv is not installed, tell the user to install it.

After installing, ensure uv is on PATH:

   source $HOME/.local/bin/env 2>/dev/null  # Add uv to current shell
   

2. FinLab is installed via uv (requires >= 1.5.9):

   uv python install 3.12  # Ensure Python is available (skip if already installed)
   uv pip install --system "finlab>=1.5.9" 2>/dev/null || uv pip install "finlab>=1.5.9"
   

Or use uv run for zero-setup execution (recommended for one-off scripts):

   uv run --with "finlab" python3 script.py
   

uv run --with auto-creates a temporary environment with dependencies — no venv management needed.

3. API Token is set (required - finlab will fail without it):

If no token, use finlab's built-in login (available in >= 1.5.9):

   import finlab
   finlab.login()  # Opens browser for Google OAuth, saves token automatically
   

This handles the full OAuth flow (browser login, token retrieval, .env storage) automatically.

Language

Respond in the user's language. If user writes in Chinese, respond in Chinese. If in English, respond in English.

API Token Tiers & Usage

Token Tiers

| Tier | Daily Limit | Token Pattern | | ---- | ----------- | ----------------- | | Free | 500 MB | ends with #free | | VIP | 5000 MB | no suffix |

Usage Reset

Quick Start Example

from finlab import data
from finlab.backtest import sim
1. Fetch data
close = data.get("price:收盤價")
vol = data.get("price:成交股數")
pb = data.get("price_earning_ratio:股價淨值比")
2. Create conditions
cond1 = close.rise(10)  # Rising last 10 days
cond2 = vol.average(20) > 1000*1000  # High liquidity
cond3 = pb.rank(axis=1, pct=True) < 0.3  # Low P/B ratio
3. Combine conditions and select stocks
position = cond1 & cond2 & cond3
position = pb[position].is_smallest(10)  # Top 10 lowest P/B
4. Backtest
report = sim(position, resample="M", upload=False)
5. Print metrics - Two equivalent ways:
Option A: Using metrics object
print(report.metrics.annual_return())
print(report.metrics.sharpe_ratio())
print(report.metrics.max_drawdown())
Option B: Using get_stats() dictionary (different key names!)
stats = report.get_stats()
print(f"CAGR: {stats['cagr']:.2%}")
print(f"Sharpe: {stats['monthly_sharpe']:.2f}")
print(f"MDD: {stats['max_drawdown']:.2%}")
report

Core Workflow: 5-Step Strategy Development

Step 1: Fetch Data

Use data.get("

from finlab import data
Price data
close = data.get("price:收盤價")
volume = data.get("price:成交股數")
Financial statements
roe = data.get("fundamental_features:ROE稅後")
revenue = data.get("monthly_revenue:當月營收")
Valuation
pe = data.get("price_earning_ratio:本益比")
pb = data.get("price_earning_ratio:股價淨值比")
Institutional trading
foreign_buy = data.get("institutional_investors_trading_summary:外陸資買賣超股數(不含外資自營商)")
Technical indicators
rsi = data.indicator("RSI", timeperiod=14)
macd, macd_signal, macd_hist = data.indicator("MACD", fastperiod=12, slowperiod=26, signalperiod=9)

Filter by market/category using data.universe():

# Limit to specific industry
with data.universe(market='TSE_OTC', category=['水泥工業']):
    price = data.get('price:收盤價')
Set globally
data.set_universe(market='TSE_OTC', category='半導體')

See data-reference.md for complete data catalog.

Step 2: Create Factors & Conditions

Use FinLabDataFrame methods to create boolean conditions:

# Trend
rising = close.rise(10)  # Rising vs 10 days ago
sustained_rise = rising.sustain(3)  # Rising for 3 consecutive days
Moving averages
sma60 = close.average(60)
above_sma = close > sma60
Ranking
top_market_value = data.get('etl:market_value').is_largest(50)
low_pe = pe.rank(axis=1, pct=True) < 0.2  # Bottom 20% by P/E
Industry ranking
industry_top = roe.industry_rank() > 0.8  # Top 20% within industry

See dataframe-reference.md for all FinLabDataFrame methods.

Step 3: Construct Position DataFrame

Combine conditions with & (AND), | (OR), ~ (NOT):

# Simple position: hold stocks meeting all conditions
position = cond1 & cond2 & cond3
Limit number of stocks
position = factor[condition].is_smallest(10)  # Hold top 10
Entry/exit signals with hold_until
entries = close > close.average(20)
exits = close < close.average(60)
position = entries.hold_until(exits, nstocks_limit=10, rank=-pb)

Important: Position DataFrame should have:

Step 4: Backtest

from finlab.backtest import sim
Basic backtest
report = sim(position, resample="M")
With risk management
report = sim(
    position,
    resample="M",
    stop_loss=0.08,
    take_profit=0.15,
    trail_stop=0.05,
    position_limit=1/3,
    fee_ratio=1.425/1000/3,
    tax_ratio=3/1000,
    trade_at_price='open',
    upload=False
)
Extract metrics - Two ways:
Option A: Using metrics object
print(f"Annual Return: {report.metrics.annual_return():.2%}")
print(f"Sharpe Ratio: {report.metrics.sharpe_ratio():.2f}")
print(f"Max Drawdown: {report.metrics.max_drawdown():.2%}")
Option B: Using get_stats() dictionary (note: different key names!)
stats = report.get_stats()
print(f"CAGR: {stats['cagr']:.2%}")           # 'cagr' not 'annual_return'
print(f"Sharpe: {stats['monthly_sharpe']:.2f}") # 'monthly_sharpe' not 'sharpe_ratio'
print(f"MDD: {stats['max_drawdown']:.2%}")     # same name

See backtesting-reference.md for complete sim() API.

Step 5: Execute Orders (Optional)

Convert backtest results to live trading:

from finlab.online.order_executor import Position, OrderExecutor
from finlab.online.sinopac_account import SinopacAccount
1. Convert report to position
position = Position.from_report(report, fund=1000000)
2. Connect broker account
acc = SinopacAccount()
3. Create executor and preview orders
executor = OrderExecutor(position, account=acc)
executor.create_orders(view_only=True)  # Preview first
4. Execute orders (when ready)
executor.create_orders()

See trading-reference.md for complete broker setup and OrderExecutor API.

Reference Files

| File | Content | | -------------------------------------------------------------- | ------------------------------------------ | | data-reference.md | data.get(), data.universe(), 900+ 欄位 | | backtesting-reference.md | sim() 參數、stop-loss、rebalancing | | trading-reference.md | 券商設定、OrderExecutor、Position | | factor-examples.md | 60+ 策略範例 | | dataframe-reference.md | FinLabDataFrame 方法 | | factor-analysis-reference.md | IC、Shapley、因子分析 | | best-practices.md | 常見錯誤、lookahead bias | | machine-learning-reference.md | ML 特徵工程 |

Prevent Lookahead Bias

Critical: Avoid using future data to make past decisions:

# ✅ GOOD: Use shift(1) to get previous value
prev_close = close.shift(1)
❌ BAD: Don't use iloc[-2] (can cause lookahead)
prev_close = close.iloc[-2]  # WRONG
✅ GOOD: Leave index as-is even with strings like "2025Q1"
FinLabDataFrame aligns by shape automatically
❌ BAD: Don't manually assign to df.index
df.index = new_index  # FORBIDDEN

See best-practices.md for more anti-patterns.

Feedback

Direct users to open an issue on GitHub: https://github.com/koreal6803/finlab-ai/issues

Notes

   uv --version
   

   source $HOME/.local/bin/env 2>/dev/null  # Add uv to current shell
   

   uv python install 3.12  # Ensure Python is available (skip if already installed)
   uv pip install --system "finlab>=1.5.9" 2>/dev/null || uv pip install "finlab>=1.5.9"
   

   uv run --with "finlab" python3 script.py
   

   import finlab
   finlab.login()  # Opens browser for Google OAuth, saves token automatically