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@@ -14,11 +14,22 @@ 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") # TODO: rename file
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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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@@ -26,6 +37,7 @@ for instrument in instruments:
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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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