returns_by_region.py

import requests
import ccxt
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.ticker as mtick

symbol = "FIL/BTC"
time = "1h"

exch = ccxt.binance()

data = exch.fetchOHLCV(
    f"{symbol}", time, limit=5001
)

df = pd.DataFrame(
    data, columns=["time", "open", "high", "low", "close", "volume"]
)
df["time_utc"] = pd.to_datetime(df.time, unit="ms").astype(str)
df["symbol"] = symbol


df["logret"] = np.log(df.close/df.close.shift(1))
df["ret"] = df.close/df.close.shift(1)-1

df["date"] = pd.to_datetime(df.time, unit='ms')

# ax = plt.figure().gca()
# df.set_index("date")[["ret", "logret"]].cumsum().plot(grid=True, ax=ax)
# plt.title(symbol + " Returns")

# ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))


ax = plt.figure().gca()

df.loc[(df.date.dt.hour >= 12)&(df.date.dt.hour <=22)].set_index("date").logret.cumsum().plot(label="US")
df.loc[(df.date.dt.hour >= 22)|(df.date.dt.hour <=8)].set_index("date").logret.cumsum().plot(label="APAC")
df.loc[(df.date.dt.hour >= 8)&(df.date.dt.hour <=17)].set_index("date").logret.cumsum().plot(label="EU")
plt.legend()
plt.grid(True)

plt.title(symbol + " Returns by Region")
ax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))