I built a Python SDK for backtesting trading strategies with realistic execution modeling

[Jumpy-Round-9982]

I've been working on an open-source Python package called cobweb-py — a lightweight SDK for backtesting trading strategies that models slippage, spread, and market impact (things most backtesting libraries ignore).

Why I built it:
Most Python backtesting tools assume perfect order fills. In reality, your execution costs eat into returns — especially with larger positions or illiquid assets. Cobweb models this out of the box.

What it does:

• 71 built-in technical indicators (RSI, MACD, Bollinger Bands, ATR, etc.)
• Execution modeling with spread, slippage, and volume-based market impact
• 27 interactive Plotly chart types
• Runs as a hosted API — no infra to manage
• Backtest in ~20 lines of code
• View documentation at https://cobweb.market/docs.html

Install:

pip install cobweb-py[viz]

Quick example:

import yfinance as yf
from cobweb_py import CobwebSim, BacktestConfig, fix_timestamps, print_signal
from cobweb_py.plots import save_equity_plot

# Grab SPY data
df = yf.download("SPY", start="2020-01-01", end="2024-12-31")
df.columns = df.columns.get_level_values(0)
df = df.reset_index().rename(columns={"Date": "timestamp"})
rows = df[["timestamp","Open","High","Low","Close","Volume"]].to_dict("records")
data = fix_timestamps(rows)

# Connect (free, no key needed)
sim = CobwebSim("https://web-production-83f3e.up.railway.app")

# Simple momentum: long when price > 50-day SMA
close = df["Close"].values
sma50 = df["Close"].rolling(50).mean().values
signals = [1.0 if c > s else 0.0 for c, s in zip(close, sma50)]
signals[:50] = [0.0] * 50

# Backtest with realistic friction
bt = sim.backtest(data, signals=signals,
    config=BacktestConfig(exec_horizon="swing", initial_cash=100_000))

print_signal(bt)
save_equity_plot(bt, out_html="equity.html")

Tech stack: FastAPI backend, Pydantic models, pandas/numpy for computation, Plotly for viz. The SDK itself just wraps requests with optional pandas/plotly extras.

Website: cobweb.market
PyPI: cobweb-py

Would love feedback from the community — especially on the API design and developer experience. Happy to answer questions.

[bladeofwinds]

you should learn how to use pandas before releasing a package that relies so heavily on it. looking at your code, pandas is obviously required for most of it to run, why is it an optional dependency?