quantstats-pro 0.4.4

Drop-in replacement for QuantStats — advanced risk engines, institutional HTML tearsheets, and actionable portfolio analytics for quants

QuantStats Pro: Advanced portfolio analytics for quants

QuantStats Pro is an actively maintained drop-in replacement for QuantStats — same import quantstats as qs, hardened metrics, and a product roadmap that goes well beyond the original scope.

Where original quantstats stops at classic performance stats and a single HTML tearsheet, Pro is built to become a definitive quantitative analytics stack: advanced risk engines, new metrics, institutional-grade reports, and outputs designed to drive actionable decisions, not just describe the past.

Built for systematic traders, portfolio managers, and quant researchers who need production-grade tearsheets from a returns series.

pip install quantstats-pro

import quantstats as qs

Note: QuantStats Pro cannot coexist with the original quantstats package in the same environment — both provide the quantstats import namespace. Uninstall quantstats before installing quantstats-pro (pip uninstall quantstats).

Changelog » · Upstream » · Contributing »

Why QuantStats Pro?

Same API surface, stronger foundation — bugfixes, reliability, and a growing analytics layer that upstream does not aim to provide.

Headline additions (v0.3.0+):

Capability	What you get
html_simple	Lean equity-curve tearsheet — fast read on performance vs benchmark
html_montecarlo	Multi-model forward risk report (GBM, GARCH, Heston, bootstraps, Bayesian, …) with bust/goal probabilities and cross-model consensus
html_alpha_decay	Rolling alpha-decay monitor — z-score traffic lights, CUSUM, time-underwater, per-metric distribution charts
quantstats.montecarlo	Programmatic multi-model simulation engine behind the Montecarlo tearsheet
quantstats.alphadecay	Short-horizon rolling diagnostics engine behind the Alpha Decay tearsheet
Classic html	Full tearsheet — metrics + all charts, visually redesigned (v0.2.0+)

Package modules

Module	Purpose
quantstats.stats	50+ performance metrics (Sharpe, Sortino, drawdown, VaR, …)
quantstats.plots	Performance visualizations (returns, drawdown, heatmaps, rolling stats, …)
quantstats.reports	HTML tearsheets and metrics tables
quantstats.montecarlo	Multi-model forward simulation engine and analytics
quantstats.alphadecay	Short-horizon rolling risk analysis and decay detection
quantstats.utils	Data prep, download_returns, pandas helpers

Legacy shuffle-based Montecarlo remains at qs.stats.montecarlo() for backward compatibility. The new engine lives under quantstats.montecarlo and powers qs.reports.html_montecarlo().

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HTML Tearsheets

The main product surface in QuantStats Pro is its reporting layer: four HTML tearsheet types that turn a returns series into shareable, browser-ready analytics. All open in the browser by default, or save to disk with output="path.html".

Function	Use case
qs.reports.html(...)	Full classic tearsheet (metrics + all charts)
qs.reports.html_simple(...)	Pro-only — lean equity-curve tearsheet for quick strategy review
qs.reports.html_montecarlo(...)	Pro-only — multi-model forward risk analysis (1y horizon by default)
qs.reports.html_alpha_decay(...)	Pro-only — short-window alpha-decay health monitor (7/15/30d)

Simple tearsheet — html_simple

Focused equity-curve view with core metrics and charts. Ideal when you need a clean performance read without the full classic layout.

qs.reports.html_simple(returns, benchmark="SPY", output="qqq_simple.html")

Montecarlo tearsheet — html_montecarlo

Runs multiple stochastic models in parallel, surfaces bust/goal probabilities, cross-model consensus, and a stress envelope — forward-looking risk in one report.

qs.reports.html_montecarlo(
    returns,
    bust=-0.25,   # P(Bust): drawdown threshold
    goal=0.50,    # P(Goal): terminal return target
    sims=500,
    seed=42,
    output="qqq_montecarlo.html",
)

Alpha Decay tearsheet — html_alpha_decay

Rolling-window monitor for short-horizon drift: 10 metrics × 7/15/30-day windows, z-score traffic lights, CUSUM decay detection, and time-underwater analysis.

qs.reports.html_alpha_decay(
    returns,
    windows=(7, 15, 30),
    output="qqq_alpha_decay.html",
)

Classic full tearsheet — html

The original QuantStats report — all metrics and charts, visually redesigned in v0.2.0+.

qs.reports.html(returns, benchmark="SPY", output="qqq_full.html")

View interactive classic sample · Montecarlo docs · Alpha Decay docs

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Quick Start

import quantstats as qs

qs.extend_pandas()
returns = qs.utils.download_returns("QQQ")

# Single metric
qs.stats.sharpe(returns)
returns.sharpe()  # via extend_pandas()

# Snapshot plot
qs.plots.snapshot(returns, title="QQQ Performance", show=True)

# HTML tearsheet vs benchmark
qs.reports.html(returns, "SPY", output="qqq_report.html")
qs.reports.html_simple(returns, "SPY", output="qqq_simple.html")
qs.reports.html_montecarlo(returns, output="qqq_montecarlo.html")
qs.reports.html_alpha_decay(returns, output="qqq_alpha_decay.html")

See HTML Tearsheets above for screenshots and parameter details.

Crypto and 24/7 markets

For daily crypto data, pass periods_per_year=365 to reports and stats that annualize:

returns = qs.utils.download_returns("BTC-USD")
qs.reports.html(returns, periods_per_year=365, output="btc_report.html")
qs.stats.sharpe(returns, periods=365)

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Montecarlo Engine

quantstats.montecarlo characterises an asset with several stochastic models, simulates forward paths from each, and compares the distribution of outcomes (CAGR, max drawdown, bust/goal probabilities, CVaR).

Built-in models

Model	Name	Category
GBM	gbm	Montecarlo
Shuffle (legacy)	shuffle	Montecarlo
Bootstrap	bootstrap	Montecarlo
Block Bootstrap	block_bootstrap	Montecarlo
Jump Diffusion (Merton)	jump_diffusion	Montecarlo
GARCH(1,1)-t	garch	Montecarlo
Heston (SV)	heston	Montecarlo
Bayesian (NIG)	bayesian	Montecarlo
Bayesian Bootstrap	bayesian_bootstrap	Montecarlo
Trimmed Bootstrap (top 1%)	trimmed_1pct	Stress

Programmatic API

from quantstats.montecarlo import run_models, available_models
from quantstats.montecarlo import analytics as mca

print(available_models())
# ['bayesian', 'bayesian_bootstrap', 'block_bootstrap', 'bootstrap', ...]

results = run_models(
    returns,
    models=["gbm", "garch", "bootstrap"],
    horizon=252,      # 1 year (default: periods_per_year)
    sims=1000,
    bust=-0.25,
    goal=0.50,
    seed=42,
)

# Per-model summary row
gbm = results["gbm"]
print(gbm.summary)          # cagr_p5, cagr_median, maxdd_p95, prob_loss, cvar_5, …
print(gbm.sim_returns.shape)  # (horizon, sims)

# Cross-model consensus and stress envelope
median = mca.model_median_summary(results)
envelope = mca.conservative_envelope(results)
hist = mca.historical_summary(returns, horizon=252)

Legacy shuffle Montecarlo

The original upstream API still works — random permutation of historical returns:

mc = qs.stats.montecarlo(returns, sims=1000, bust=-0.20, goal=0.50, seed=42)
print(f"Bust probability: {mc.bust_probability:.1%}")
print(f"Goal probability: {mc.goal_probability:.1%}")
mc.plot()

Full Montecarlo documentation »

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Alpha Decay Monitor

quantstats.alphadecay tracks whether a strategy's short-term risk profile is drifting from its historical norm. It computes 10 rolling metrics over configurable windows (default 7/15/30 days):

CAGR · Volatility · Downside Vol · Max Drawdown · Mean Drawdown · Win Rate · VaR 95% · Expected Shortfall 95% · Payoff Ratio · Skew

Each metric-window cell gets a traffic-light status (Excellent / Good / Warning / Critical) based on z-scores computed on a per-metric analysis scale (log transforms for skewed metrics). The tearsheet also includes:

• Health score — count of Good/Excellent cells
• CUSUM return-decay detector
• Time underwater duration analysis
• Per-metric distribution charts with current observation vs historical mean

Programmatic API

from quantstats.alphadecay import analyze, available_metrics

print(available_metrics())
# ['cagr', 'volatility', 'downside_vol', 'max_drawdown', ...]

result = analyze(
    returns,
    windows=(7, 15, 30),
    rf=0.0,
    periods=252,
)

print(f"Health: {result.score}/{result.total} ({result.score_pct:.0f}%)")

for metric in result.metrics:
    wr = metric.windows[30]  # latest 30-day window
    print(f"{metric.spec.label}: z={wr.z_score:+.2f} → {wr.status}")

Generate the full HTML tearsheet with qs.reports.html_alpha_decay(returns).

Alpha Decay documentation »

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API reference

QuantStats Pro exposes 50+ stats, 15+ plot types, and 4 HTML report generators. Explore the full API interactively:

[f for f in dir(qs.stats) if not f.startswith("_")]
[f for f in dir(qs.plots) if not f.startswith("_")]
help(qs.stats.sharpe)

Notebook / console helpers:

qs.reports.metrics(returns, mode="full")   # metrics table
qs.reports.plots(returns, mode="full")     # all plots
qs.reports.basic(returns)                  # basic metrics + plots
qs.reports.full(returns)                   # full metrics + plots

See Montecarlo documentation and help(qs.stats.<method>) for parameter details.

Period-based vs trade-based metrics

QuantStats analyzes return series (daily, weekly, monthly returns), not discrete trade data. This means:

• Win Rate = percentage of periods with positive returns
• Consecutive Wins/Losses = consecutive positive/negative return periods
• Payoff Ratio = average winning period return / average losing period return
• Profit Factor = sum of positive returns / sum of negative returns

These metrics are valid and useful for systematic strategies, return-series analysis, and period-by-period comparison. For discretionary traders with multi-day trades, period-based stats may differ from trade-level statistics — consistent with how Sharpe, Sortino, and drawdown metrics operate on return periods.

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Installation

pip install quantstats-pro --upgrade

Requirements

• Python >= 3.10
• pandas >= 1.5.0
• numpy >= 1.24.0
• scipy >= 1.11.0
• matplotlib >= 3.7.0
• seaborn >= 0.13.0
• tabulate >= 0.9.0
• yfinance >= 0.2.40
• arch >= 6.0 (GARCH calibration for Montecarlo)
• plotly >= 5.0.0 (optional, for using plots.to_plotly())

Questions?

If you find a bug, please open an issue.

Contributions welcome — check open issues or upstream QuantStats issues for bugs we're tracking.

Known limitations

When saving the monthly returns heatmap via savefig={...}, the figure may still be displayed in addition to being written to disk. Pass show=False explicitly where supported.

Legal Stuff

QuantStats Pro is a fork of QuantStats by Ran Aroussi, distributed under the Apache Software License. See LICENSE.txt for details.

Credits

QuantStats Pro — maintained by Diego Alvarez

QuantStats (original) — Ran Aroussi