update
This commit is contained in:
David Brazda
2
.gitignore
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.gitignore
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/.venv/
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.venv
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49
.vscode/launch.json
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.vscode/launch.json
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{
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"version": "0.2.0",
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"configurations": [
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{
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"name": "Python: Attach",
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"type": "python",
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"request": "attach",
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"port": 5678, // or the port used by your Jupyter server
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"justMyCode": false
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},
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{
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"name": "Python: Aktuální soubor",
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"type": "python",
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"request": "launch",
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"program": "${file}",
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"cwd": "${workspaceFolder}",
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"env": {
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"PYTHONPATH": "${workspaceFolder}:${workspaceFolder}/bld"
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},
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"console": "integratedTerminal",
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"justMyCode": false,
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"python": "${command:python.interpreterPath}",
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"internalConsoleOptions": "openOnSessionStart"
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},
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{
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"name": "Python: Main",
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"type": "python",
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"request": "launch",
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"program": "v2realbot/main.py",
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"justMyCode": false
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},
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{
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"name": "Python: File",
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"type": "python",
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"request": "launch",
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"program": "${file}",
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"justMyCode": false
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},
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{
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"name": "Python: Attach",
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"type": "python",
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"request": "attach",
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"connect": {
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"host": "localhost",
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"port": 5678
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}
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}
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]
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}
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.vscode/settings.json
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.vscode/settings.json
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{
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"git.ignoreLimitWarning": true
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}
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61
requirements.txt
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requirements.txt
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appnope==0.1.4
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asttokens==2.4.1
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attrs==24.2.0
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certifi==2024.8.30
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charset-normalizer==3.4.0
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comm==0.2.2
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dateparser==1.2.0
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debugpy==1.8.6
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decorator==5.1.1
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exceptiongroup==1.2.2
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executing==2.1.0
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humanize==4.11.0
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idna==3.10
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imageio==2.35.1
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ipykernel==6.29.5
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ipython==8.28.0
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jedi==0.19.1
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joblib==1.4.2
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jupyter_client==8.6.3
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jupyter_core==5.7.2
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llvmlite==0.39.1
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matplotlib-inline==0.1.7
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mypy-extensions==1.0.0
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nest-asyncio==1.6.0
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numba==0.56.4
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numpy==1.23.5
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packaging==24.1
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pandas==2.2.3
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parso==0.8.4
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pexpect==4.9.0
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pillow==10.4.0
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platformdirs==4.3.6
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prompt_toolkit==3.0.48
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psutil==6.0.0
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ptyprocess==0.7.0
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pure_eval==0.2.3
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Pygments==2.18.0
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python-dateutil==2.9.0.post0
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pytz==2024.2
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pyzmq==26.2.0
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regex==2024.9.11
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requests==2.32.3
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schedule==1.2.2
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scikit-learn==1.5.2
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scipy==1.14.1
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six==1.16.0
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stack-data==0.6.3
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TA-Lib==0.4.32
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threadpoolctl==3.5.0
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tomli==2.0.2
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tornado==6.4.1
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tqdm==4.66.5
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traitlets==5.14.3
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ttools @ git+https://github.com/drew2323/ttools.git@6833710e66967948c711614108f8f5b3c7f16294
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typing_extensions==4.12.2
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tzdata==2024.2
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tzlocal==5.2
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urllib3==2.2.3
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vectorbtpro @ file:///Users/davidbrazda/Downloads/vectorbtpro-2024.6.19-py3-none-any.whl#sha256=5e202f62aeb7697bb1411302e1c062d1df4fed5de5e1c2e8867eac07263c2d4a
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wcwidth==0.2.13
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websocket-client==1.8.0
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0
vbt-helper.ipynb
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0
vbt-helper.ipynb
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139
vbt.py
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139
vbt.py
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import vectorbtpro as vbt
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import lightweightcharts
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#region DATA
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#fetching from remote db
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from lib.db import Connection
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SYMBOL = "BAC"
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SCHEMA = "ohlcv_1s" #time based 1s other options ohlcv_vol_200 (volume based ohlcv with resolution of 200), ohlcv_renko_20 (renko with 20 bricks size) ...
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DB = "market_data"
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con = Connection(db_name=DB, default_schema=SCHEMA, create_db=True)
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basic_data = con.pull(symbols=[SYMBOL], schema=SCHEMA,start="2024-08-01", end="2024-08-08", tz_convert='America/New_York')
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#endregion
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#region DISCOVERY
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#get parameters of method
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vbt.phelp(vbt.indicator("talib:MOM").run)
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vbt.IF.list_indicators("*sma")
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#endregion
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#region RESAMPLING
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from vectorbtpro.utils.config import merge_dicts, Config, HybridConfig
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from vectorbtpro import _typing as tp
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from vectorbtpro.generic import nb as generic_nb
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_feature_config: tp.ClassVar[Config] = HybridConfig(
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{
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"buyvolume": dict(
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resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(
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resampler,
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generic_nb.sum_reduce_nb,
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)
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),
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"sellvolume": dict(
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resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(
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resampler,
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generic_nb.sum_reduce_nb,
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)
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),
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"trades": dict(
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resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(
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resampler,
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generic_nb.sum_reduce_nb,
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)
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)
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}
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)
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basic_data._feature_config = _feature_config
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t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','trades','sellvolume']].resample("1T")
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t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())
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#realign closing
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resampler_s = vbt.Resampler(t1data.index, s1data.index, source_freq="1T", target_freq="1s")
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t1close_realigned = t1data.data["BAC"].close.vbt.realign_closing(resampler_s)
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#endregion
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#region ENTRIES/EXITS
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# window open/close
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#END OF DAY EXITS
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# end_of_day_dates = index.to_series().resample("1d").last().values
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# exit_signals.loc[end_of_day_dates] = True
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end_of_day_dates = open_hours_index.to_series().resample("1d").last()
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df['exit'][df['exit'].index.isin(end_of_day_dates)] = True
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# This index should be probably open_hours_index
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# But also check that end_of_day_dates doesn't have nans (NaT), and if it has, you need to filter them out (edited)
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#endregion
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#region INDICATORS
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#anchored VWAP
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t1vwap_h = vbt.VWAP.run(t1data.high, t1data.low, t1data.close, t1data.volume, anchor="H")
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t1vwap_d = vbt.VWAP.run(t1data.high, t1data.low, t1data.close, t1data.volume, anchor="D")
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t1vwap_t = vbt.VWAP.run(t1data.high, t1data.low, t1data.close, t1data.volume, anchor="T")
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t1vwap_h_real = t1vwap_h.vwap.vbt.realign_closing(resampler_s)
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t1vwap_d_real = t1vwap_d.vwap.vbt.realign_closing(resampler_s)
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t1vwap_t_real = t1vwap_t.vwap.vbt.realign_closing(resampler_s)
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#SMA - note for TALIB use skipna=True
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mom_multi_beztf = vbt.indicator("talib:MOM").run(t1data.close, timeperiod=5, skipna=True)
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#TALIB INDICATORS can do realing closing : timeframe=["1T"]
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mom_multi = vbt.indicator("talib:MOM").run(t1data.close, timeperiod=5, timeframe=["1T","5T"], skipna=True) #returned 5T can be directly compared with 1T
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#ANCHORED indciators vbt.indicator("pandas_ta:BBANDS") is called AnchoredIndicator("pandas_ta:BBANDS")
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from ttools import AnchoredIndicator
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#BBANDS = vbt.indicator("pandas_ta:BBANDS")
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mom_anch_d = AnchoredIndicator("talib:MOM", anchor='30min').run(t1data.data["BAC"].close, timeperiod=10)
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mom = vbt.indicator("talib:MOM").run(t1data.data["BAC"].close, timeperiod=10, skipna=True)
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#macd = vbt.indicator("talib:MACD").run(t1data.data["BAC"].close) #, timeframe=["1T"]) #,
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t1data.ohlcv.data["BAC"].lw.plot(auto_scale=[mom_anch_d, mom])
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#endregion
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#region CHARTING
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#LW df/sr accessor
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t1data.ohlcv.data["BAC"].lw.plot(left=[(mom_multi, "mom_multi")]) #OHLCV with indicators on top
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t5data.ohlcv.data["BAC"].lw.plot(
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left=[(mom_multi.real, "mom"),(mom_multi_beztf, "mom_beztf"), (mom_5t_orig, "mom_5t_orig"), (mom_5t_orig_realigned, "mom_5t_orig_realigned")],
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right=[(t1data.data["BAC"].close, "t1 close"),(t5data.data["BAC"].close, "t5 close")],
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size="s") #.loc[:,(20,"1T","BAC")]
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#PANEL
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pane1 = Panel(
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#auto_scale=[mom_multi, mom_multi_1t],
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#ohlcv=(t1data.data["BAC"],), #(series, entries, exits, other_markers)
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#histogram=[(order_imbalance_allvolume, "oivol")], # [(series, name, "rgba(53, 94, 59, 0.6)", opacity)]
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right=[(t1data.data["BAC"].close,"close 1T"),(t5data.data["BAC"].close,"close 5T"),(mom_multi_1t.close, "mom multi close")], # [(series, name, entries, exits, other_markers)]
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left=[(mom_multi, "mom_multi"), (mom_multi_1t, "mom_multi_1t")],
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#middle1=[],
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#middle2=[],
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#xloc="2024-02-12 09:30",
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precision=3
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)
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ch = chart([pane1], size="s")
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#endregion
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