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import os
import sys
import pandas as pd
from finta import TA
def add_time_feature(df, symbol, dt_col_name="time"):
"""read csv into df and index on time
dt_col_name can be any unit from minutes to day. time is the index of pd
must have pd columns [(time_col),(asset_col), Open,close,High,Low,day]
data_process will add additional time information: time(index), minute, hour, weekday, week, month,year, day(since 1970)
use StopLoss and ProfitTaken to simplify the action,
feed a fixed StopLoss (SL = 200) and PT = SL * ratio
action space: [action[0,2],ratio[0,10]]
rewards is point
add hourly, dayofweek(0-6, Sun-Sat)
Args:
file (str): file path/name.csv
"""
df["symbol"] = symbol
df["dt"] = pd.to_datetime(df[dt_col_name])
df.index = df["dt"]
df["minute"] = df["dt"].dt.minute
df["hour"] = df["dt"].dt.hour
df["weekday"] = df["dt"].dt.dayofweek
df["week"] = df["dt"].dt.isocalendar().week
df["month"] = df["dt"].dt.month
df["year"] = df["dt"].dt.year
df["day"] = df["dt"].dt.day
# df = df.set_index('dt')
return df
# 'macd', 'boll_ub', 'boll_lb', 'rsi_30', 'dx_30','close_30_sma', 'close_60_sma'
def tech_indictors(df):
df["macd"] = TA.MACD(df).SIGNAL
df["boll_ub"] = TA.BBANDS(df).BB_UPPER
df["boll_lb"] = TA.BBANDS(df).BB_LOWER
df["rsi_30"] = TA.RSI(df, period=30)
df["dx_30"] = TA.ADX(df, period=30)
df["close_30_sma"] = TA.SMA(df, period=30)
df["close_60_sma"] = TA.SMA(df, period=60)
# fill NaN to 0
df = df.fillna(0)
print(
f"--------df head - tail ----------------\n{df.head(3)}\n{df.tail(3)}\n---------------------------------"
)
return df
def split_timeserious(df, key_ts="dt", freq="W", symbol=""):
"""import df and split into hour, daily, weekly, monthly based and
save into subfolder
Args:
df (pandas df with timestamp is part of multi index):
spliter (str): H, D, W, M, Y
"""
freq_name = {
"H": "hourly",
"D": "daily",
"W": "weekly",
"M": "monthly",
"Y": "Yearly",
}
for count, (n, g) in enumerate(df.groupby(pd.Grouper(level=key_ts, freq=freq))):
p = f"./data/split/{symbol}/{freq_name[freq]}"
os.makedirs(p, exist_ok=True)
# fname = f'{symbol}_{n:%Y%m%d}_{freq}_{count}.csv'
fname = f"{symbol}_{n:%Y}_{count}.csv"
fn = f"{p}/{fname}"
print(f"save to:{fn}")
g.reset_index(drop=True, inplace=True)
g.drop(columns=["dt"], inplace=True)
g.to_csv(fn)
return
"""
python ./neo_finrl/data_processors/fx.py GBPUSD W ./data/raw/GBPUSD_raw.csv
symbol="GBPUSD"
freq = [H, D, W, M]
file .csv, column names [time, Open, High, Low, Close, Vol]
"""
if __name__ == "__main__":
symbol, freq, file = sys.argv[1], sys.argv[2], sys.argv[3]
print(f"processing... symbol:{symbol} freq:{freq} file:{file}")
try:
df = pd.read_csv(file)
except Exception:
print(f"No such file or directory: {file}")
exit(0)
df = add_time_feature(df, symbol=symbol, dt_col_name="time")
df = tech_indictors(df)
split_timeserious(df, freq=freq, symbol=symbol)
print(f"Done!")
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