chore: import upstream snapshot with attribution

Tests/TestData/generate_reference_data_from_tulip.py

@@ -0,0 +1,51 @@
+import numpy as np
+import pandas as pd
+import tulipy as ti
+
+
+def write_dataframe(df, output_names, fname_stem):
+    # convert columns to np.int64
+    for col in ["open", "high", "low", "close", "volume"]:
+        df[col] = df[col].astype(np.int64)
+    df = df.reset_index()
+    df["date"] = df["datetime"].dt.strftime("%m/%d/%Y")
+    df["time"] = df["datetime"].dt.strftime("%H:%M")
+    df = df[["date", "time", "open", "high", "low", "close", "volume", *output_names]]
+    df.to_csv(f"{fname_stem}.csv")
+
+
+def generate_reference_data_for_siso_indicator(
+    df, indicator_type, parameters, output_name, fname_stem
+):
+    # get close column as numpy array
+    input_array = df["close"].values
+    # get output array
+    output_array = indicator_type(input_array, **parameters)
+    # when the size of the output array is less than the input array, insert zeros at the beginning
+    missings = len(input_array) - len(output_array)
+    output_array = np.insert(output_array, 0, np.zeros(missings))
+    df[output_name] = output_array
+    write_dataframe(df, [output_name], fname_stem)
+
+
+def main():
+    fname = "../../Data/equity/usa/daily/spy.zip"
+    df = pd.read_csv(
+        fname,
+        names=["datetime", "open", "high", "low", "close", "volume"],
+    )
+    # convert datetime string to datetime64[ns]
+    df["datetime"] = pd.to_datetime(df["datetime"], format="%Y%m%d %H:%M")
+    # convert columns to np.float64
+    for col in ["open", "high", "low", "close", "volume"]:
+        df[col] = df[col].astype(np.float64)
+    df = df.set_index("datetime")
+    df = df[["open", "high", "low", "close", "volume"]]
+
+    generate_reference_data_for_siso_indicator(
+        df, ti.tsf, {"period": 5}, "tsf", "spy_tsf"
+    )
+
+
+if __name__ == "__main__":
+    main()