market-structure 0.1.0

Python library for market structure analysis — swings, trends, support/resistance zones, break/retest/flip signals. Works with any OHLCV DataFrame.

market-structure

Python library for market structure analysis — swings, trends, support/resistance zones, break/retest/flip signals. Works with any OHLCV DataFrame. Includes a Freqtrade integration.

Live Demo — interactive chart viewer showing market structure overlays on real price data.

Quick start

import pandas as pd
from market_structure.hydrate import hydrate
from market_structure.tsi import compute_tsi
from market_structure.atr import _compute_atr

# df: any OHLCV DataFrame with open, high, low, close, volume columns
df["tsi_hist"] = compute_tsi(df["close"])["tsi_histogram"]
helper = hydrate(df, histogram_key="tsi_hist")

atr = _compute_atr(df["high"].to_numpy(), df["low"].to_numpy(),
                    df["close"].to_numpy(), period=14)

for z in helper.get_support_zones(atr_arr=atr):
    body_low, body_high = z.range       # body-anchored (accepted price)
    wick_low, wick_high = z.wick_range   # wick extrema (for stop placement)
    print(f"{z.anchor_wave_id}  body=({body_low:.2f}, {body_high:.2f})  "
          f"wick=({wick_low:.2f}, {wick_high:.2f})  double={z.is_double}")

Freqtrade integration

For Freqtrade strategies, a single call projects all 67 ms_* columns onto the DataFrame:

from market_structure.freqtrade import attach_market_structure

df = attach_market_structure(df, histogram_key="tsi_hist")
# df now has ms_support_zone_low, ms_wave_side, ms_is_trending_up, etc.

Backtest: market structure as a strategy filter

To validate the library, we ran an A/B comparison using a TSI signal-line crossover strategy on 4h Binance futures (BTC, ETH, SOL, XRP, LTC) over 360 days in a -14.91% market. The only difference: the "With MS" variant filters entries and exits through the ms_* columns; the "Without" variant uses the same TSI signal and risk parameters but skips market structure entirely.

Metric	With MS filter	Without MS filter
Total profit	+39.85%	+22.59%
Profit factor	1.50	1.21
Sharpe	0.94	0.58
Max drawdown	17.76%	21.75%
Trades	164	208
Win rate	36.0%	31.7%
Stop-loss hits	36	46

The filter blocked 44 low-quality entries (mostly would-be stop-outs), nearly doubling profit while cutting drawdown by ~4 percentage points. Backtest data and results for all three strategies live in refs/freqtrade/.

Disclaimer: This is educational software, not financial advice. Past backtest performance does not guarantee future results. Parameters are curve-fit to a specific historical window. Do not trade real capital based on these examples without independent validation and risk assessment.

Documentation

• Column Reference — all 67 ms_* columns projected onto the DataFrame, with dtypes, tier descriptions, and strategy examples.

Install

pip install market-structure

The PyPI distribution is market-structure; the import name is market_structure. The GitHub repo is named pymarket-structure.

Development

This project uses uv for everything: Python version management, virtual environment, dependencies, lockfile, and dev tooling. Install uv first (one line — see the link above), then:

git clone [e-mail]:fortunato/pymarket-structure.git
cd pymarket-structure
uv sync                            # creates .venv, installs all deps from uv.lock
uv run pre-commit install          # one-time: enable git pre-commit hooks
uv run just                        # list all available development commands
uv run just check                  # run lint + format-check + type + test

That's the entire bootstrap. Everything else is described by the project files themselves:

• pyproject.toml — package metadata, dependencies, ruff/pyright/pytest configuration
• Justfile — development commands (run uv run just for the full menu)
• .pre-commit-config.yaml — git hook configuration
• .editorconfig — editor formatting rules
• .python-version — pinned Python version

Tech stack

Tool	Purpose
uv	Package manager, virtual environment, Python version manager
ruff	Linter + formatter (replaces black, isort, flake8, pylint)
pyright	Strict static type checker
pytest + pytest-cov	Test runner with branch coverage
pre-commit	Git hook framework
just	Task runner

Common tasks

Once uv sync has run, the day-to-day workflow is driven by just recipes. A few highlights:

uv run just check              # full CI pipeline (lint + format + type + test)
uv run just test               # run all tests
uv run just test -k swing      # run only tests matching a pattern
uv run just lint               # ruff lint with auto-fix
uv run just fmt                # ruff format
uv run just type               # pyright (strict mode)
uv run just hooks              # run all pre-commit hooks against every file

If your shell has the project's venv activated (source .venv/bin/activate), you can drop the uv run prefix and just type just check, pytest, ruff check, etc. directly. Most editors (PyCharm, VS Code) auto-activate the venv once the interpreter is configured.

Support

If this library saves you time or helps your trading, consider buying me a coffee.

License

See LICENSE.