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56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import argparse
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import os
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import pandas as pd
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import numpy as np
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import pickle
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parser = argparse.ArgumentParser()
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parser.add_argument("--seed", type=int, default=20220926)
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parser.add_argument("--num_order", type=int, default=10)
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args = parser.parse_args()
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np.random.seed(args.seed)
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path = os.path.join("data", "pickle", "backtesttest.pkl")
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df = pickle.load(open(path, "rb")).reset_index()
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df["date"] = df["datetime"].dt.date.astype("datetime64")
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instruments = sorted(set(df["instrument"]))
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# TODO: The example is expected to be able to handle data containing missing values.
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# TODO: Currently, we just simply skip dates that contain missing data. We will add
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# TODO: this feature in the future.
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skip_dates = {}
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for instrument in instruments:
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csv_df = pd.read_csv(os.path.join("data", "csv", f"{instrument}.csv"))
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csv_df = csv_df[csv_df["close"].isna()]
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dates = set([str(d).split(" ")[0] for d in csv_df["date"]])
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skip_dates[instrument] = dates
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df_list = []
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for instrument in instruments:
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print(instrument)
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cur_df = df[df["instrument"] == instrument]
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dates = sorted(set([str(d).split(" ")[0] for d in cur_df["date"]]))
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dates = [date for date in dates if date not in skip_dates[instrument]]
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n = args.num_order
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df_list.append(
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pd.DataFrame(
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{
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"date": sorted(np.random.choice(dates, size=n, replace=False)),
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"instrument": [instrument] * n,
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"amount": np.random.randint(low=3, high=11, size=n) * 100.0,
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"order_type": np.random.randint(low=0, high=2, size=n),
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}
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).set_index(["date", "instrument"]),
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)
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total_df = pd.concat(df_list)
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total_df.to_csv("data/backtest_orders.csv")
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