Backtesting with Zipline

Fonrex ships a zipline-reloaded data bundle, zipline_bundle/, that reads the daily prices of your database directly — no CSV export, no parallel dataset. The API never imports Zipline: install it only where you run the backtests.

Prerequisites

• Prices ingested in your instance (POST /historical/ingest, scripts/ingest_all.py).
• The Fonrex repository and zipline-reloaded in the same Python environment:

pip install zipline-reloaded

• Access to the database. With Docker Compose it is published on 127.0.0.1:5432 of the host:

export DATABASE_URL="postgresql://fonrex:<POSTGRES_PASSWORD>@localhost:5432/fonrex"

Register and ingest the bundle

mkdir -p ~/.zipline
cp zipline_bundle/extension.py ~/.zipline/extension.py
zipline bundles            # fonrex <no ingestions>
zipline ingest -b fonrex

Or without the extension file:

python -m zipline_bundle ingest --start 2020-01-01 --end 2025-12-31 \
    --tickers AAPL,MSFT --calendar NYSE

# What the bundle would contain (Zipline not needed)
python -m zipline_bundle preview --start 2024-01-01 --end 2024-12-31

Variable	Default	Description
DATABASE_URL	the address of .env.example	Database read by the bundle (postgresql+asyncpg:// is accepted)
FONREX_BUNDLE_NAME	fonrex	Bundle name
FONREX_BUNDLE_TICKERS	(empty)	Comma-separated tickers; empty = every instrument with daily prices in the window
FONREX_BUNDLE_CALENDAR	NYSE	Trading calendar: XPAR, XETR, XLON, XSWX… for other markets

For several markets, register one bundle per calendar:

from zipline_bundle import register_fonrex_bundle

register_fonrex_bundle(bundle_name="fonrex_us", tickers=["AAPL", "MSFT"], calendar_name="NYSE")
register_fonrex_bundle(bundle_name="fonrex_paris", tickers=["AIR.PA", "BNP.PA"], calendar_name="XPAR")

Run a backtest

import pandas as pd
from zipline import run_algorithm
from zipline.api import order_target_percent, symbol

def initialize(context):
    context.asset = symbol("AIR.PA")

def handle_data(context, data):
    order_target_percent(context.asset, 1.0)

result = run_algorithm(
    start=pd.Timestamp("2024-01-02"),
    end=pd.Timestamp("2024-12-31"),
    initialize=initialize,
    handle_data=handle_data,
    capital_base=100_000,
    bundle="fonrex_paris",
)

What the bundle contains

• Daily bars only, from prices_eod.
• One listing per instrument: the primary one, then an active one; the other listings (other currencies) are not exposed.
• Adjusted prices: adj_close (adjusted for splits and dividends) is used as the Zipline close, and the open, high and low of the bar are scaled by the same factor; a bar without adj_close (TradingView) keeps its prices. Split and dividend tables are written empty.
• Calendar alignment: bars outside the sessions of the calendar are dropped.
• Stable sids: assigned in the alphabetical order of the symbols.

Without Zipline: pandas

For Backtrader, vectorbt or your own code, read the prices through the API:

import os
import pandas as pd
import requests

def fonrex_ohlcv(ticker: str, period: str = "5y") -> pd.DataFrame:
    response = requests.get(
        f"http://localhost:5000/eod/{ticker}",
        params={"period": period},
        headers={"X-API-KEY": os.environ["FONREX_API_KEY"]},
        timeout=60,
    )
    response.raise_for_status()
    frame = pd.DataFrame(response.json()["data"])
    frame["Date"] = pd.to_datetime(frame["Date"])
    return frame.set_index("Date").rename(columns=str.lower)

df = fonrex_ohlcv("AIR.PA")
print(df.tail())

The columns are open, high, low, close, adj close and volume, one row per trading session.