backtest360 0.1.0

Official Python client for the Backtest360 API

backtest360

Official Python client for the Backtest360 API.

Run backtests, automate, integrate with your research or trading workflows. The SDK handles authentication, serialization, and error mapping so you can call a single method instead of building and parsing the raw HTTP requests yourself.

import matplotlib.pyplot as plt
import yfinance as yf
from backtest360 import Client, Strategy

df = yf.download("BTC-USD", period="1y", interval="1d",
                 auto_adjust=False, multi_level_index=False, progress=False)
df.columns = df.columns.str.lower()

result = Client(api_key="b360_...").backtest(Strategy.rsi_threshold_long(), df)
result.summary()
result.strategy_equity.plot(title="Equity curve")
plt.show()

Save this snippet as quickstart.py, drop in your API key, and run it with python quickstart.py (install matplotlib if needed: pip install matplotlib).

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Install

pip install backtest360

Requires Python 3.9+. The only runtime dependencies are httpx and pandas.

Need to install Python first? (Windows / macOS / Linux / WSL)

Windows (native)

1. Install Python from python.org/downloads (check "Add to PATH"), or via winget:

   winget install Python.Python.3.12
   

2. Open PowerShell and verify:

   python --version
   

3. Install the SDK and yfinance for the quickstart:

   pip install backtest360 yfinance
   

4. Run a script:

   python quickstart.py
   

Windows via WSL (recommended for quant work)

1. Install WSL:

   wsl --install
   

   Restart, then open the Ubuntu terminal.
2. Install Python:

   sudo apt update && sudo apt install python3 python3-pip -y
   

3. Install the SDK:

   pip3 install backtest360 yfinance
   

4. Run a script:

   python3 quickstart.py
   

macOS

# via Homebrew (recommended)
brew install python

# or download from python.org/downloads

Then:

pip3 install backtest360 yfinance
python3 quickstart.py

Linux

sudo apt install python3 python3-pip -y   # Debian/Ubuntu
pip3 install backtest360 yfinance
python3 quickstart.py

Get an API key

Sign up at backtest360.com/api and copy your key. Store it in the BACKTEST360_API_KEY environment variable or pass it directly:

client = Client(api_key="b360_...")
# or: export BACKTEST360_API_KEY=b360_...
client = Client()

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Features

• Synchronous client — straightforward to use in scripts, notebooks, and pipelines
• Hand-written wrapper over the public REST API — no generated code, no schema sync
• Built-in strategy templates (Strategy.rsi_threshold_long(), Strategy.ma_crossover(), …)
• Grouped-knob classes: Execution, Costs, Risk, Sizing, MarketHours, Settings — set only what you need
• Pre-computed signals path: client.backtest_signals(series, df) for model-generated signals
• Pandas-native — pass a DataFrame, get pandas Series back (result.strategy_equity, result.returns)
• Engine introspection: client.version() and client.health() for uptime checks and version compatibility
• Raw-API escape hatch for full control (client.backtest_raw({...}))
• Strict type hints + py.typed — first-class IDE and mypy support
• MIT licensed

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Common patterns

Custom strategy

from backtest360 import Client, Strategy, Execution, Costs, Risk, Sizing, Settings

strat = Strategy(
    name="rsi_mean_reversion",
    long_entry="rsi < 30",
    long_exit="rsi > 70",
    indicators=[Strategy.indicator("rsi", period=14)],
)

result = Client(api_key="b360_...").backtest(
    strat, df,
    benchmark=spy_df,
    execution=Execution(entry="open", exit="close", signal_frequency="daily"),
    costs=Costs(slippage_bps=2.5, fee_pct=0.001),
    risk=Risk(stop="atr", value=2.5, atr_period=14, max_drawdown=0.25),
    sizing=Sizing(weight=1.0, vol_target=0.15, leverage_limit=2.0),
    settings=Settings(risk_free_rate=0.04),
)

result.summary()
print(result.stats["Max Drawdown"])
for t in result.trades[:5]:
    print(t["entry_date"], t["direction"], t["return_net"])

Indicator library (names, params, output columns): https://api.backtest360.com/docs#tag/Reference/operation/list_indicators_api_indicators_get

Strategy templates (full list): https://api.backtest360.com/docs#tag/Reference/operation/list_strategies_api_strategies_get

Pre-computed signals

import pandas as pd
from backtest360 import Client

# Any signal series of {-1, 0, 1} — your ML model, custom indicator, etc.
signals = pd.Series(..., index=df.index)

result = Client(api_key="b360_...").backtest_signals(signals, df)
print(result.stats["Sharpe"])

Raw API escape hatch

For users who want exact control with the API docs open:

resp = Client(api_key="...").backtest_raw({
    "strategy":    {"condition_tree": {...}, "indicators": [...]},
    "data_source": {"ohlcv": {...}},
    "execution":   {"signal_frequency": "daily"},
})

Error handling

from backtest360 import Backtest360Error

try:
    result = client.backtest(strategy, df)
except Backtest360Error as e:
    if e.status == 401:
        print("Invalid or expired API key — renew at backtest360.com/api")
    elif e.status == 429:
        print("Rate limited — retry after a moment")
    elif e.status == 422:
        print("Strategy validation failed:", e.body)
    else:
        raise   # unexpected — let it propagate

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Versioning

MAJOR.MINOR.PATCH. Pre-1.0 (0.x.y): the API may move between minor versions. Pre-release suffixes: aN (alpha), bN (beta), rcN (release candidate). See CHANGELOG.md for the release history.

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Full documentation

Full documentation → https://backtest360.github.io/backtest360/

Engine API reference → https://api.backtest360.com/docs

Questions / feedback

Questions or feedback? [e-mail] — we read everything. The SDK is in active development, so help shape it.

Bug reports and feature requests: open an issue on GitHub.

License

MIT — see LICENSE.