Python quant research · iPhone + iPad

Research ideas.
See the code.

Explore financial data, test quantitative ideas, and understand every result directly in Python. PyRun Quant is built for research and education—not trading calls.

Coming to the App Store Real Python · pandas · NumPy · Matplotlib

For research and educational purposes only. Not investment advice.

PyRun Quant showing Python research and a backtest on iPad

Next Open Adjusted Prices Costs Included Benchmark Compared

One transparent workflow

From market history to an answer you can inspect.

Start with fictional demo instruments, choose a research question, then open the exact Python behind the result. Nothing is hidden behind a strategy builder.

Explore Demo Markets

Study deterministic, synthetic daily data designed to make drawdowns, costs, and correlations visible.

Simulated data

Ask a research question

Try five focused templates—from trend following to why look-ahead bias can turn a loser into a winner.

Open the real Python

Edit ordinary pandas code, run it in the on-device CPython runtime, and inspect output, charts, and tracebacks.

Programmable by design

The result and the code are the same product.

No miniature strategy language. Every dataset is a pandas DataFrame, every indicator is a Series, and every template remains yours to change.

from pyrun.finance import stock, backtest, SMA

df = stock("NOVA").history("10y")
fast = SMA(df.close, 20)
slow = SMA(df.close, 50)

result = backtest(
    df,
    entry=fast > slow,
    exit=fast < slow,
    benchmark="DEMO500",
)
result.report()

Honest by default

A backtest should be allowed to say “no.”

PyRun Quant does not tune the included templates to manufacture a flattering return. The benchmark and assumptions travel with every result.

No same-bar shortcut

Signals are formed at a bar’s close and filled at the next bar’s open. There is no switch that quietly introduces look-ahead bias.

Prices adjusted

Returns account for splits and dividends, so a corporate action is not misread as a market crash.

Trading costs charged

Commission and slippage are non-zero by default and visible in the assumptions.

Benchmark always present

A strategy result appears beside its benchmark, even when simply holding did better.

Built for Apple screens

Quant research without a desktop detour.

Move from Markets to Research to Python Lab in one native workspace. Your code executes locally; the built-in Demo Market works without a network connection.

Native financial charts
Five inspectable research templates
Editor, Console, pandas, NumPy, and Matplotlib

Research · backtest · understand

Keep the method visible.

Coming to the App Store

Research ideas.See the code.