Financial Data API

Financial Data API for Quant Research & Backtesting

30+ years of point-in-time data. No look-ahead bias. Survivorship-bias-free. Built for systematic traders.

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Point-in-time data

No look-ahead bias. Survivorship-bias-free.

01

No look-ahead bias

Data as it was known at each date. Fundamentals, prices, and macro — point-in-time for every query.

02

Survivorship-bias-free

Delisted companies included. Full universe for backtests. No survivorship bias in your research.

Data coverage

30+ years. Delisted companies. Corporate actions.

01

30+ years history

OHLCV from 1990. Fundamentals, macro, fixed income. Decades of clean time series.

02

Delisted companies

Full universe for backtests. No survivorship bias. M&A, bankruptcies, delistings included.

03

Corporate actions

Splits, dividends, spin-offs. Adjusted prices. Restated financials as reported.

04

Factor research ready

Value, momentum, quality, size. Build factors with point-in-time data.

Python example

Factor research code

Python
import eulerpool
import pandas as pd

# Point-in-time fundamentals for value factor
client = eulerpool.Client("ep_live_xxx")
universe = client.equity.universe()

# Backtest-safe: data as known at each rebalance date
fundamentals = client.equity.fundamentals_bulk(
    symbols=universe,
    as_of="2020-01-31"
)

# Build value factor: E/P, B/P, etc.
df = pd.DataFrame(fundamentals)
df["ep_ratio"] = 1 / df["pe_ratio"]
value_quintile = df.groupby("as_of")["ep_ratio"].transform(pd.qcut, 5, labels=[1,2,3,4,5])

Features

Built for quant workflows

01

Historical depth

30+ years OHLCV, fundamentals, macro. From 1990 to present.

02

Batch endpoints

Query 100+ symbols in one call. Efficient for universe screens.

03

Data quality

Point-in-time, survivorship-bias-free. Backtesting-safe by design.

04

Python SDK

Native pandas integration. Works with backtrader, zipline, vectorbt.

Frequently asked questions

Yes. Eulerpool provides point-in-time, survivorship-bias-free data designed for quantitative research. 30+ years of historical prices, financial statements, and macroeconomic data. The Python SDK returns pandas DataFrames for seamless integration with quant workflows.

Yes. Pre-calculated financial ratios (P/E, P/B, ROE, ROA, momentum, volatility) and point-in-time fundamentals enable Fama-French, momentum, quality, and custom factor research. Delisted companies are included to avoid survivorship bias in factor returns.

Yes. Historical OHLCV data with split and dividend adjustments, corporate action events, and point-in-time fundamentals provide everything needed for backtesting. The API integrates with popular backtesting frameworks like backtrader, Zipline, and QuantConnect via the Python SDK.

Delisted companies remain in the dataset with their complete price and fundamental history up to the delisting date. This ensures backtests and factor studies reflect the actual investable universe at each historical point, avoiding the inflated returns caused by survivorship bias.

The Python SDK integrates with pandas, numpy, scipy, statsmodels, scikit-learn, and TensorFlow for statistical and ML research. Backtesting frameworks (backtrader, Zipline), visualization tools (matplotlib, plotly), and Jupyter notebooks are fully supported. The API also works with R via direct HTTP calls.

Backtest on 30+ years of clean data.

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No credit card required. Point-in-time data included.

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