Your first strategy in 10 minutes

This lesson takes you through FinLab's essential strategy tools, from data to backtest results, in about ten minutes. You will write one complete US strategy with entry and exit rules, backtest it, then dig into its trades and statistics.

Build and backtest the strategy

from finlab import data
from finlab.backtest import sim
from finlab.markets.us import USMarket

from finlab import data
from finlab.markets.us import USMarket

data.set_market('us')

close = data.get('price:adj_close')
close = close[close.index.dayofweek < 5]  # remove weekends
close.iloc[:5, :5]

profile = data.get('us_company_profile')
profile.head(3)

from finlab import data
from finlab.backtest import sim
from finlab.markets.us import USMarket

data.set_market('us')

close = data.get('price:adj_close')
close = close[close.index.dayofweek < 5]  # remove weekends

# Price-to-book: market value / stockholders' equity
pb = data.get('etl:us_market_value') / data.get(
    'us_balance_sheet:total_stockholders_equity'
)
pb = pb[pb > 0]

sma20 = close.average(20)
sma60 = close.average(60)

entries = close > sma20
exits = close < sma60

position = entries.hold_until(exits, nstocks_limit=10, rank=-pb, take_profit=0.4)
backtest_report = sim(position, market=USMarket(), fee_ratio=0.001, tax_ratio=0)

backtest_report = sim(position, market=USMarket(), fee_ratio=0.001, tax_ratio=0)
backtest_report.display()

Dig into the results

Each snippet below uses backtest_report from the steps above.

Cumulative return series

The creturn attribute is a time series of cumulative returns, useful for custom plots or further analysis.

backtest_report.creturn

Current holdings and the next rebalance

position_info() shows the latest portfolio positions and any changes expected at the next rebalancing date.

backtest_report.position_info()

Trade records

get_trades() returns a DataFrame with the entry and exit details of every completed trade.

trade_record = backtest_report.get_trades()
trade_record.head(5)

Key columns:

Return distribution

From the trade records, plot the distribution of trade returns to see the balance between winning and losing trades.

import plotly.express as px

trade_record['profit_loss'] = trade_record['return'].apply(
    lambda s: 'profit' if s > 0 else 'loss'
)
win_ratio = round(
    sum(trade_record['profit_loss'] == 'profit')
    / len(trade_record['profit_loss'])
    * 100,
    2,
)
return_mean = round(trade_record['return'].mean(), 2)
fig = px.histogram(
    trade_record,
    x="return",
    color="profit_loss",
    title=f'profit_loss_hist - win_ratio: {win_ratio} %',
)
fig.add_vline(
    x=return_mean,
    line_width=3,
    line_dash="dash",
    line_color="green",
    annotation_position="top left",
    annotation_text=f'avg_return:{return_mean}',
    row=1,
    col=1,
)

Liquidity risk

LiquidityAnalysis checks whether the strategy's trades are realistic given actual market liquidity. It reports what fraction of trades exceed the volume and turnover thresholds you set, which helps you gauge the strategy's capacity.

from finlab.analysis.liquidityAnalysis import LiquidityAnalysis

backtest_report.run_analysis(
    LiquidityAnalysis(required_volume=100000, required_turnover=1000000)
)

Strategy statistics

get_stats() returns a full set of performance metrics, including the Sharpe ratio, Sortino ratio, maximum drawdown and return statistics.

backtest_report.get_stats()

How to read the key metrics:

• Sharpe ratio: below zero, the strategy loses money on a risk-adjusted basis. For a typical stock selection strategy:
  • 0.7 is achievable for most discretionary swing traders;
  • 0.9 is solid for an ETF-like systematic strategy;
  • 1.3+ is the target for a well-designed quantitative strategy;
  • 2.0+ is an exceptionally strong strategy (rare, and often capacity-constrained).
• Kurtosis (kurt): compared with a normal (Gaussian) distribution, higher kurtosis means a more peaked return distribution, typically more consistent performance with occasional extreme events. For the broad market, kurtosis is roughly 1–2. Above 2 is acceptable for a strategy, and above 5 is quite good.
• Skewness: more negative values mean a left tail (occasional large losses). Below -0.5 is acceptable, and around -1 is decent.

What you covered

1. Data: data.set_market('us') and data.get() load price and fundamental data.
2. Strategy: boolean entry and exit signals, combined with hold_until() to manage positions.
3. Backtest: sim() with USMarket() simulates the US market realistically.
4. Analysis: cumulative returns, trade records, return distributions, liquidity checks and performance statistics.

Each part is modular: you can swap in different data, entry and exit logic, or analysis methods without changing the rest of the pipeline.

Next steps