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