update

.gitignore

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+/.venv/
+.venv

.vscode/launch.json

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+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Attach",
+            "type": "python",
+            "request": "attach",
+            "port": 5678,  // or the port used by your Jupyter server
+            "justMyCode": false
+        },
+        {
+            "name": "Python: Aktuální soubor",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "cwd": "${workspaceFolder}",
+            "env": {
+                "PYTHONPATH": "${workspaceFolder}:${workspaceFolder}/bld"
+            },
+            "console": "integratedTerminal",
+            "justMyCode": false,
+            "python": "${command:python.interpreterPath}",
+            "internalConsoleOptions": "openOnSessionStart"
+        },
+        {
+            "name": "Python: Main",
+            "type": "python",
+            "request": "launch",
+            "program": "v2realbot/main.py",
+            "justMyCode": false
+        },
+        {
+            "name": "Python: File",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "justMyCode": false
+        },
+        {
+            "name": "Python: Attach",
+            "type": "python",
+            "request": "attach",
+            "connect": {
+                "host": "localhost",
+                "port": 5678
+            }
+        }
+    ]
+}

.vscode/settings.json

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+{
+    "git.ignoreLimitWarning": true
+}

requirements.txt

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+appnope==0.1.4
+asttokens==2.4.1
+attrs==24.2.0
+certifi==2024.8.30
+charset-normalizer==3.4.0
+comm==0.2.2
+dateparser==1.2.0
+debugpy==1.8.6
+decorator==5.1.1
+exceptiongroup==1.2.2
+executing==2.1.0
+humanize==4.11.0
+idna==3.10
+imageio==2.35.1
+ipykernel==6.29.5
+ipython==8.28.0
+jedi==0.19.1
+joblib==1.4.2
+jupyter_client==8.6.3
+jupyter_core==5.7.2
+llvmlite==0.39.1
+matplotlib-inline==0.1.7
+mypy-extensions==1.0.0
+nest-asyncio==1.6.0
+numba==0.56.4
+numpy==1.23.5
+packaging==24.1
+pandas==2.2.3
+parso==0.8.4
+pexpect==4.9.0
+pillow==10.4.0
+platformdirs==4.3.6
+prompt_toolkit==3.0.48
+psutil==6.0.0
+ptyprocess==0.7.0
+pure_eval==0.2.3
+Pygments==2.18.0
+python-dateutil==2.9.0.post0
+pytz==2024.2
+pyzmq==26.2.0
+regex==2024.9.11
+requests==2.32.3
+schedule==1.2.2
+scikit-learn==1.5.2
+scipy==1.14.1
+six==1.16.0
+stack-data==0.6.3
+TA-Lib==0.4.32
+threadpoolctl==3.5.0
+tomli==2.0.2
+tornado==6.4.1
+tqdm==4.66.5
+traitlets==5.14.3
+ttools @ git+https://github.com/drew2323/ttools.git@6833710e66967948c711614108f8f5b3c7f16294
+typing_extensions==4.12.2
+tzdata==2024.2
+tzlocal==5.2
+urllib3==2.2.3
+vectorbtpro @ file:///Users/davidbrazda/Downloads/vectorbtpro-2024.6.19-py3-none-any.whl#sha256=5e202f62aeb7697bb1411302e1c062d1df4fed5de5e1c2e8867eac07263c2d4a
+wcwidth==0.2.13
+websocket-client==1.8.0

vbt.py

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+import vectorbtpro as vbt
+import lightweightcharts
+
+#region DATA
+ #fetching from remote db
+from lib.db import Connection
+SYMBOL = "BAC"
+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) ...
+DB = "market_data"
+
+con = Connection(db_name=DB, default_schema=SCHEMA, create_db=True)
+basic_data = con.pull(symbols=[SYMBOL], schema=SCHEMA,start="2024-08-01", end="2024-08-08", tz_convert='America/New_York')
+#endregion
+
+#region DISCOVERY
+#get parameters of method
+vbt.phelp(vbt.indicator("talib:MOM").run)
+
+vbt.IF.list_indicators("*sma")
+
+#endregion
+
+#region RESAMPLING
+from vectorbtpro.utils.config import merge_dicts, Config, HybridConfig
+from vectorbtpro import _typing as tp
+from vectorbtpro.generic import nb as generic_nb
+
+_feature_config: tp.ClassVar[Config] = HybridConfig(
+    {
+        "buyvolume": dict(
+            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(
+                resampler,
+                generic_nb.sum_reduce_nb,
+            )
+        ),
+        "sellvolume": dict(
+            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(
+                resampler,
+                generic_nb.sum_reduce_nb,
+            )
+        ),
+        "trades": dict(
+            resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(
+                resampler,
+                generic_nb.sum_reduce_nb,
+            )
+        )
+    }
+)
+
+basic_data._feature_config = _feature_config
+
+t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','trades','sellvolume']].resample("1T")
+t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())
+
+#realign closing
+resampler_s = vbt.Resampler(t1data.index, s1data.index, source_freq="1T", target_freq="1s")
+t1close_realigned = t1data.data["BAC"].close.vbt.realign_closing(resampler_s)
+
+
+
+#endregion
+
+#region ENTRIES/EXITS
+
+# window open/close 
+
+
+
+#END OF DAY EXITS
+# end_of_day_dates = index.to_series().resample("1d").last().values
+# exit_signals.loc[end_of_day_dates] = True
+end_of_day_dates = open_hours_index.to_series().resample("1d").last()
+df['exit'][df['exit'].index.isin(end_of_day_dates)] = True
+# This index should be probably open_hours_index
+# But also check that end_of_day_dates doesn't have nans (NaT), and if it has, you need to filter them out (edited)
+
+#endregion
+
+
+#region INDICATORS
+#anchored VWAP
+t1vwap_h = vbt.VWAP.run(t1data.high, t1data.low, t1data.close, t1data.volume, anchor="H")
+t1vwap_d = vbt.VWAP.run(t1data.high, t1data.low, t1data.close, t1data.volume, anchor="D")
+t1vwap_t = vbt.VWAP.run(t1data.high, t1data.low, t1data.close, t1data.volume, anchor="T")
+
+t1vwap_h_real = t1vwap_h.vwap.vbt.realign_closing(resampler_s)
+t1vwap_d_real = t1vwap_d.vwap.vbt.realign_closing(resampler_s)
+t1vwap_t_real = t1vwap_t.vwap.vbt.realign_closing(resampler_s)
+
+#SMA - note for TALIB use skipna=True
+mom_multi_beztf = vbt.indicator("talib:MOM").run(t1data.close, timeperiod=5, skipna=True)
+
+#TALIB INDICATORS can do realing closing : timeframe=["1T"]
+mom_multi = vbt.indicator("talib:MOM").run(t1data.close, timeperiod=5, timeframe=["1T","5T"], skipna=True) #returned 5T can be directly compared with 1T
+
+#ANCHORED indciators vbt.indicator("pandas_ta:BBANDS") is called AnchoredIndicator("pandas_ta:BBANDS")
+from ttools import AnchoredIndicator
+
+#BBANDS = vbt.indicator("pandas_ta:BBANDS")
+mom_anch_d = AnchoredIndicator("talib:MOM", anchor='30min').run(t1data.data["BAC"].close, timeperiod=10)
+mom = vbt.indicator("talib:MOM").run(t1data.data["BAC"].close, timeperiod=10, skipna=True)
+#macd = vbt.indicator("talib:MACD").run(t1data.data["BAC"].close) #, timeframe=["1T"]) #, 
+t1data.ohlcv.data["BAC"].lw.plot(auto_scale=[mom_anch_d, mom])
+
+
+
+#endregion
+
+
+#region CHARTING
+
+#LW df/sr accessor
+t1data.ohlcv.data["BAC"].lw.plot(left=[(mom_multi, "mom_multi")]) #OHLCV with indicators on top
+
+
+t5data.ohlcv.data["BAC"].lw.plot(
+    left=[(mom_multi.real, "mom"),(mom_multi_beztf, "mom_beztf"), (mom_5t_orig, "mom_5t_orig"), (mom_5t_orig_realigned, "mom_5t_orig_realigned")],
+    right=[(t1data.data["BAC"].close, "t1 close"),(t5data.data["BAC"].close, "t5 close")],
+    size="s") #.loc[:,(20,"1T","BAC")]
+
+#PANEL
+pane1 = Panel(
+    #auto_scale=[mom_multi, mom_multi_1t],
+    #ohlcv=(t1data.data["BAC"],),  #(series, entries, exits, other_markers)
+    #histogram=[(order_imbalance_allvolume, "oivol")], # [(series, name, "rgba(53, 94, 59, 0.6)", opacity)]
+    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)]
+    left=[(mom_multi, "mom_multi"), (mom_multi_1t, "mom_multi_1t")],
+    #middle1=[],
+    #middle2=[],
+    #xloc="2024-02-12 09:30",
+    precision=3
+)
+
+ch = chart([pane1], size="s")
+
+
+
+#endregion