cobweb-py 0.1.7

Python SDK for the CobwebSim stock simulation and backtesting API. Compute 71 technical features, run backtests with realistic execution modeling, and generate 27 plot types.

cobweb-py

Python SDK for realistic backtesting with CobwebSim.

Your backtest is lying to you. Most backtesters only model transaction fees. cobweb-py adds the friction costs they miss: bid-ask spread, slippage, and market impact.

Features

• 71 technical features computed server-side (RSI, SMA, EMA, ATR, Bollinger, MACD, and more)
• Realistic execution modeling with fees, spread, slippage, and market impact
• 27 interactive Plotly charts (equity curves, drawdowns, regime analysis, trade logs)
• Works with any OHLCV data — CSV files, pandas DataFrames, or yfinance

Installation

pip install cobweb-py

With visualization support (Plotly charts):

pip install cobweb-py[viz]

Requires Python 3.9+. No API key required.

Quick Start

from cobweb_py import CobwebSim, BacktestConfig, fix_timestamps, to_signals, score_by_id
import yfinance as yf

# 1. Connect
sim = CobwebSim("https://web-production-83f3e.up.railway.app")

# 2. Download data
df = yf.download("AAPL", 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)

# 3. Enrich with technical features
feats = sim.enrich(data, feature_ids=[1, 11, 36])
enriched_rows = feats["rows"]

# 4. Score and generate signals
scores = score_by_id(enriched_rows, {11: 0.3, 36: 0.3, 1: 0.4})
signals = to_signals(scores, entry_th=0.20, exit_th=0.05, use_shorts=False)

# 5. Backtest with realistic friction
bt = sim.backtest(data, signals=signals, config=BacktestConfig(
    initial_cash=10_000,
    exec_horizon="swing",
    fee_bps=1.0,
))

print(f"Return: {bt['metrics']['total_return']:.2%}")
print(f"Sharpe: {bt['metrics']['sharpe_ann']:.2f}")
print(f"Max DD: {bt['metrics']['max_drawdown']:.2%}")

Examples

See cobweb-py-examples for 8 runnable Jupyter notebook examples.

API Reference

CobwebSim Client

sim = CobwebSim("https://web-production-83f3e.up.railway.app")

Method	Description
sim.health()	Check API status
sim.enrich(data, feature_ids=[...])	Compute technical features
sim.backtest(data, signals, config=...)	Run backtest with realistic execution
sim.plots(data, bt, plot_ids=[...])	Generate interactive charts

BacktestConfig

BacktestConfig(
    initial_cash=10_000,       # Starting capital
    exec_horizon="swing",      # intraday | swing | longterm
    fee_bps=1.0,               # Broker fee (basis points)
    half_spread_bps=2.0,       # Bid-ask half-spread
    base_slippage_bps=1.0,     # Slippage
    impact_coeff=1.0,          # Market impact multiplier
    allow_margin=False,        # Allow short positions
    max_leverage=1.0,          # Maximum leverage
)

Scoring & Signals

# Weight features by ID
scores = score_by_id(rows, {11: 0.3, 36: 0.3, 14: 0.4})

# Convert to trading signals
signals = to_signals(scores, entry_th=0.20, exit_th=0.05, use_shorts=False)

Pipeline (High-Level)

from cobweb_py import Pipeline

pipe = Pipeline("https://web-production-83f3e.up.railway.app", "stock.csv")
result = pipe.run(weights={36: 0.3, 11: 0.3, 1: 0.4})
print(result["metrics"])

Helper Functions

Function	Description
fix_timestamps(rows)	Normalize dates to ISO format
load_csv(path)	Load CSV into API-ready format
align(base, benchmark)	Align two datasets by timestamp
to_signals(scores, entry_th, exit_th)	Convert scores to buy/hold/sell
score_by_id(rows, weights)	Weighted feature scoring
print_signal(bt)	Print current signal
save_all_plots(sim, data, bt, plot_ids)	Save interactive HTML charts
show_features()	Print feature reference table
show_plots()	Print plot reference table

Documentation

Full documentation at cobweb.market/docs.html.

License

MIT