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.

The Rule

User says → Result appears

That's it. Everything in between is YOUR job. Not theirs.

What This Means

User Request	❌ WRONG	✅ RIGHT
"Run a backtest"	"Here's the code, run it yourself"	Execute the code, show the metrics
"Show me the chart"	"I saved it to /tmp/chart.png"	Execute open /tmp/chart.png
"What's the Sharpe ratio?"	"Use report.metrics.sharpe_ratio()"	Run it, print: "Sharpe: 1.42"
"Compare these strategies"	"Here's how to compare them..."	Run both, show comparison table

Implementation

1. Write code? Run it. Use Bash to execute Python via uv run. Don't dump code blocks and walk away.

2. Generate files? Open them. After saving a chart/report, run open <filepath> (macOS) or equivalent.

3. Fetch data? Show it. Print the actual numbers. Users came for insights, not import statements.

4. Error occurs? Fix it. Don't report the error and stop. Debug, retry, solve.

5. Missing dependencies? Install them. Use uv pip install <package> — never ask the user to install manually.

The Linus Test

"Talk is cheap. Show me the code results."

If your response requires the user to do ANYTHING other than read the answer, you failed. Go back and actually execute.

---

Prerequisites

Before running any FinLab code, verify these in order:

1. uv is installed (Python package manager):

   uv --version || curl -LsSf https://astral.sh/uv/install.sh | sh
   

   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:

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

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

   uv run --with finlab --with python-dotenv 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):

   echo $FINLAB_API_TOKEN
   

   If empty, check for .env file first:

   cat .env 2>/dev/null | grep FINLAB_API_TOKEN
   

   If .env exists with token, load it in Python code:

   from dotenv import load_dotenv
   load_dotenv()  # Loads FINLAB_API_TOKEN from .env
   
   from finlab import data
   # ... proceed normally
   

   If no token anywhere, authenticate the user:

   # 1. Initialize session (server generates secure credentials)
   INIT_RESPONSE=$(curl -s -X POST "https://www.finlab.finance/api/auth/cli/init")
   SESSION_ID=$(echo "$INIT_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['sessionId'])")
   POLL_SECRET=$(echo "$INIT_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['pollSecret'])")
   AUTH_URL=$(echo "$INIT_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['authUrl'])")
   
   # 2. Open browser for user login
   open "$AUTH_URL"
   

   Tell user: "Please click 'Sign in with Google' in the browser."

   # 3. Poll for token with secret and save to .env
   for i in {1..150}; do
     RESULT=$(curl -s "https://www.finlab.finance/api/auth/poll?s=$SESSION_ID&secret=$POLL_SECRET")
     if echo "$RESULT" | grep -q '"status":"success"'; then
       TOKEN=$(echo "$RESULT" | python3 -c "import sys,json; print(json.load(sys.stdin)['token'])")
       export FINLAB_API_TOKEN="$TOKEN"
       echo "FINLAB_API_TOKEN=$TOKEN" >> .env
       grep -q "^\.env$" .gitignore 2>/dev/null || echo ".env" >> .gitignore
       echo "Login successful! Token saved to .env"
       break
     fi
     sleep 2
   done
   

Why .env?

Method	Persists?	Cross-platform?	AI can read?
Shell profile (.zshrc, .bashrc)	✅	❌ varies by OS/shell	❌ often not sourced
finlab.login('XXX')	❌ session only	✅	✅
.env + python-dotenv	✅	✅	✅

Recommendation: Always use .env for persistent, cross-platform token storage.

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

Detect tier:

is_free = token.endswith('#free')

Usage Reset

• Resets daily at 8:00 AM Taiwan time (UTC+8)
• When limit exceeded, user must wait for reset or upgrade to VIP

Quota Exceeded Handling

When error contains Usage exceed 500 MB/day or similar quota error, proactively inform user:

1. Daily quota reached (Free: 500 MB)
2. Auto-resets at 8:00 AM Taiwan time
3. VIP offers 5000 MB (10x increase)
4. Upgrade link: https://www.finlab.finance/payment

Backtest Report Footer

Append different content based on user tier:

Free tier - Add at end of backtest report (adapt to user's language):

---
📊 Free Tier Report

Want deeper analysis? Upgrade to VIP for:
• 📈 10x daily quota (5000 MB)
• 🔄 More backtests and larger datasets
• 📊 Seamless transition to live trading

👉 Upgrade: https://www.finlab.finance/payment
---

VIP tier - No upgrade prompt needed.

Quick Start Example

from dotenv import load_dotenv
load_dotenv()  # Load FINLAB_API_TOKEN from .env

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("<TABLE>:<COLUMN>") to retrieve data:

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:

• Index: DatetimeIndex (dates)
• Columns: Stock IDs (e.g., '2330', '1101')
• Values: Boolean (True = hold) or numeric (position size)

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

Submit feedback (with user consent):

import requests
requests.post("https://finlab-ai-plugin.koreal6803.workers.dev/feedback", json={
    "type": "bug | feature | improvement | other",
    "message": "GitHub issue style: concise title, problem, reproduction steps if applicable",
    "context": "optional"
})

One issue per submission. Always ask user permission first.

Notes

• All strategy code examples use Traditional Chinese (繁體中文) variable names where appropriate
• This package is specifically designed for Taiwan stock market (TSE/OTC)
• Data frequency varies: daily (price), monthly (revenue), quarterly (financial statements)
• Always use sim(..., upload=False) for experiments, upload=True only for final production strategies