Pratik Bhadane
63 lines
1.6 KiB
ReStructuredText
63 lines
1.6 KiB
ReStructuredText
Benchmarks
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==========
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The authoritative benchmark workflow is in ``benchmarks/``:
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- Cross-library speed suite: ``benchmarks/test_speed.py``
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- Cross-library accuracy suite: ``benchmarks/test_accuracy.py``
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- TA-Lib head-to-head speed script: ``benchmarks/bench_vs_talib.py``
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- Table generation from benchmark JSON: ``benchmarks/benchmark_table.py``
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Run the cross-library speed suite on 100,000 bars:
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.. code-block:: bash
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uv run pytest benchmarks/test_speed.py --benchmark-only --benchmark-json=benchmarks/results.json -v
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Selected results on a modern CPU (100,000 bars):
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.. list-table::
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:header-rows: 1
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* - Indicator
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- Throughput
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* - ``ADD``
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- 1.9 G bars/s
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* - ``CDLENGULFING``
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- 454 M bars/s
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* - ``EMA``
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- 444 M bars/s
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* - ``SMA``
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- 259 M bars/s
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* - ``RSI``
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- 145 M bars/s
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* - ``ATR``
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- 70 M bars/s
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* - ``MACD``
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- 104 M bars/s
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* - ``STOCH``
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- 33 M bars/s
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Multi-size and JSON output
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--------------------------
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To build the markdown comparison table from the JSON output:
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.. code-block:: bash
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uv run python benchmarks/benchmark_table.py
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Comparison with TA-Lib
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----------------------
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To measure speedup vs TA-Lib on the same data and parameters, run:
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.. code-block:: bash
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pip install ta-lib
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python benchmarks/bench_vs_talib.py --sizes 10000 100000 --json benchmark_vs_talib.json
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See the README “Performance vs TA-Lib” section for methodology and a
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representative comparison table. The script prints a table of median times and
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speedup (TA-Lib time / ferro_ta time); use ``--json out.json`` to save results.
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