Initial commit

README.md

@@ -1,2 +1,7 @@
 # ttools
 A Python library for tools, utilities, and helpers for my trading research workflow.
+
+# Modules
+
+- `utils.vbtutils` - contains helpers for vbtpro
+  - `AnchoredIndicator` - allows runing any vbt indicator in anchored mode (reset by Day, Hour etc.)

setup.py

@@ -0,0 +1,11 @@
+from setuptools import setup, find_packages
+
+setup(
+    name='ttools',
+    version='0.1.0',
+    packages=find_packages(),
+    install_requires=[
+        'vectorbtpro',
+        # list your dependencies here
+    ],
+)

utils/vbtutils.py

@@ -0,0 +1,50 @@
+import pandas as pd
+import vectorbtpro as vbt
+
+class AnchoredIndicator:
+    """
+    Allows to run any VBT indicator in anchored mode (reset per Day, Hour, or Minute).
+    """
+    def __init__(self, indicator_name: str, split_by='D'):
+        """
+        Initialize with the name of the indicator (e.g., "talib:MOM").
+        
+        Parameters:
+        - indicator_name: str, the name of the vectorbt indicator.
+        - split_by: str, 'D' for day, 'H' for hour, 'T' for minute (default is 'D').
+        """
+        self.indicator_name = indicator_name
+        self.indicator = vbt.indicator(indicator_name)
+        self.split_by = split_by
+
+    def run(self, data, split_by=None, *args, **kwargs):
+        """
+        Run the indicator on a Series or DataFrame by splitting it by day, hour, or minute,
+        applying the indicator to each group, and concatenating the results.
+        
+        Parameters:
+        - data: pd.Series or pd.DataFrame, the input data series or dataframe (e.g., close prices).
+        - split_by: str, 'D' for day, 'H' for hour, 'T' for minute (default is 'D').
+        - *args, **kwargs: Arguments and keyword arguments passed to the indicator.
+        """
+
+        if split_by is None:
+            split_by = self.split_by
+
+        # Group by the specified frequency
+        if split_by == 'D':
+            grouped_data = data.groupby(data.index.date)
+        elif split_by in ['H', 'T']:
+            grouped_data = data.groupby(pd.Grouper(freq=split_by))
+        else:
+            raise ValueError("Invalid split_by value. Use 'D' (day), 'H' (hour), or 'T' (minute).")
+
+        # Run the indicator function for each group and concatenate the results
+        results = []
+        for date, group in grouped_data:
+            # Run the indicator for each group's data
+            result = self.indicator.run(group, *args, **kwargs)
+            results.append(result)
+
+        # Concatenate the results and return
+        return vbt.base.merging.row_stack_merge(results)