freshmeat-heikinashi 0.1.1

Fast Heikin-Ashi candle computation with Numba JIT acceleration.

freshmeat-heikinashi

Fast Heikin-Ashi candle computation with Numba JIT acceleration.

A drop-in replacement for technical.candles.heikinashi() and qtpylib.indicators.heikinashi() — same formula, hundreds to thousands of times faster.

Why?

The standard Heikin-Ashi implementations in the Python trading ecosystem use pure-Python for loops or list comprehensions over pandas DataFrames. The recursive nature of ha_open (each bar depends on the previous bar) makes vectorization impossible with plain NumPy/pandas.

freshmeat-heikinashi solves this by compiling the loop with Numba JIT, achieving native-code speed while keeping a pure-Python API.

Installation

pip install freshmeat-heikinashi

Requirements: Python 3.10+, NumPy, pandas, Numba.

Usage

DataFrame API

from freshmeat_heikinashi import heikinashi

ha = heikinashi(df)  # df must have open/high/low/close columns
# Returns a new DataFrame with HA open/high/low/close

NumPy array API

from freshmeat_heikinashi import heikinashi_arrays

ha_open, ha_high, ha_low, ha_close = heikinashi_arrays(
    open_prices, high_prices, low_prices, close_prices,
)

Freqtrade strategy

from freshmeat_heikinashi import heikinashi

class MyStrategy(IStrategy):
    def populate_indicators(self, dataframe, metadata):
        ha = heikinashi(dataframe)
        dataframe["ha_open"] = ha["open"]
        dataframe["ha_high"] = ha["high"]
        dataframe["ha_low"] = ha["low"]
        dataframe["ha_close"] = ha["close"]
        return dataframe

Correctness

Output is verified to be bit-identical (within 1e-10 tolerance) to both technical.candles.heikinashi() and qtpylib.indicators.heikinashi() across 1k, 10k, and 100k row datasets:

pytest tests/test_benchmark.py -k "TestCorrectnessVsTechnical" -v

Benchmark

Latest local pytest-benchmark result on 100,000 OHLC rows (min time):

Implementation	Min time	Relative
freshmeat-heikinashi (Numba)	1.65 ms	1x
technical.candles.heikinashi (list comp)	423.50 ms	256x slower
qtpylib.indicators.heikinashi (for loop)	2,239.60 ms	1,354x slower

The absolute values are machine-dependent, but the speedup pattern is consistent.

Run the benchmarks yourself:

# pytest-benchmark (grouped comparison)
pytest tests/test_benchmark.py -v

# CLI script
python benchmarks/bench_heikinashi.py --rows 100000

Documentation

• Heikin-Ashi Formula — step-by-step formula explanation with worked example, lookahead safety analysis.
• Implementation Comparison — detailed source code comparison of qtpylib, technical.candles, and freshmeat-heikinashi with full benchmark tables.

Status

Experimental. This project is under active development. The API may change without notice between minor versions until 1.0.

Disclaimer

This software is provided for educational and research purposes only. It is not financial advice. The authors make no guarantees regarding the accuracy, reliability, or suitability of this software for any particular purpose, including but not limited to live trading.

Use at your own risk. The authors accept no responsibility or liability for any financial losses, trading losses, damages, or other consequences — direct or indirect — arising from the use of this software. This includes, without limitation, any commercial or trading losses incurred through live, paper, or backtested trading operations. Always validate outputs independently before making any trading decisions.

License

Apache License 2.0