finlab

FinLab Quantitative Trading Package

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 >= 2.0.0):

   uv python install 3.12  # Ensure Python is available (skip if already installed)
   uv pip install --system "finlab>=2.0.0" 2>/dev/null || uv pip install "finlab>=2.0.0"
   

   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.

   Prefer zero-install? Run notebooks directly in FinLab Studio — a hosted Jupyter environment with finlab preinstalled and your API token already wired up.

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, improved Firebase flow in v1.5.11):

   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. Tokens are bound to a FinLab account at finlab.finance — finlab.login() provisions one on first use.

Language

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

Market Support

FinLab supports TW (default), US, KR, JP, HK, plus Taiwan emerging (rotc) and Taiwan convertible bonds (tw_cb). Pick the market once per session with data.set_market(<code>); generic dataset names like price:收盤價 or monthly_revenue:當月營收 resolve to the active market's tables, so strategy code is written the same way across markets. data.set_market('rotc') (v2.0.9) enables 興櫃 (TW emerging) — use it when you need pre-listing price action or revenue factors that don't exist in the main TSE/OTC catalog.

The rest of this file plus dataframe-reference.md, backtesting-reference.md, best-practices.md, factor-analysis-reference.md, and machine-learning-reference.md are market-agnostic — the APIs behave the same across markets.

For US-market work — whether single-name equities (data.set_market('us')) or ETFs/funds (data.set_market('us_fund')) — read us-market.md first. Queries that should trigger it include: US equity, S&P 500, NASDAQ 100, 美股, SPY / QQQ, sector SPDRs, leveraged / inverse ETFs, ETF rotation, us_price:*, us_fund_price:*, data.us_universe(...), or us_income_statement:* / us_cash_flow:* / us_balance_sheet:*. It documents:

• Which US data tables are safe for backtesting versus current-snapshot-only (analyst consensus, ratios, DCF are live-only — do not use them historically)
• Filing-date-aligned quarterly fundamentals (key_date == filing_date) — no .shift() workaround needed
• Report API names on US (creturn / daily_creturn / get_stats(); no get_equity())
• US backtest defaults for both markets: USMarket (fee_ratio=0, tax_ratio=0, trade_at_price='close') and USFundMarket for ETF/fund backtests
• How data.set_market(...) is the session-scope switch (there is no market= kwarg on data.get())
• Dollar-volume-top-N universe construction (works back to 2016), S&P 500 / NASDAQ 100 membership via data.us_universe(index='S&P 500' | 'NASDAQ 100') with its 2022-11 history-start caveat, quality gates, and sector-exclusion rationale
• Lookahead-bias checklist specific to US data (rolling-window universe filters, survivorship avoidance)
• ETF / sector-rotation backtesting via USFundMarket and us_fund_price:*

Other-market queries can skip that file.

API Token Tiers & Usage

Token Tiers

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

Usage Reset

• Resets daily at 8:00 AM UTC+8
• When limit exceeded, user must wait for reset or upgrade to VIP at finlab.finance

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%}")

# Benchmark stats (finlab >= 2.0.17): same keys via report.get_benchmark_stats()

# 6. Write the FinLab-generated HTML report (REQUIRED — do not hand-roll your own HTML)
report.to_html("report.html")
print("Open report.html to inspect equity curve, monthly returns, drawdown, and trade list.")

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')

Use data.search('keyword', market='<market>') to discover available datasets. Supported markets: tw, us, kr, jp, hk. Use keywords in the dataset's native language (e.g. data.search('營收', market='tw'), data.search('revenue', market='us')).

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

# Benchmark comparison (finlab >= 2.0.17): same ffn keys, computed on the
# market benchmark over the backtest period — no need to recompute from
# market.get_benchmark()
bench = report.get_benchmark_stats()
print(f"Benchmark CAGR: {bench['cagr']:.2%} | MDD: {bench['max_drawdown']:.2%}")

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

Step 4.5: Deliver the FinLab HTML Report (REQUIRED)

Follow each backtest the user will review with one HTML file — the one FinLab generates:

report = sim(position, resample="M", upload=False)
report.to_html("report.html")            # the FinLab-generated file is the deliverable

The canon

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