def sensor():
print('in sensor and alive')
try:
if os.path.exists('owid-covid-data.xlsx'):
os.remove('owid-covid-data.xlsx')
if os.path.exists('owid-covid-data.csv'):
os.remove('owid-covid-data.csv')
if os.path.exists('data.csv'):
os.remove('data.csv')
if os.path.exists('datawoarea.csv'):
os.remove('datawoarea.csv')
if os.path.exists('last.csv'):
os.remove('last.csv')
except:
print('not')
global actcase
global sstate
global cs
global d
global de
actcase, sstate, cs, d, de = pull_data.pull()
df = pd.read_excel('owid-covid-data.xlsx', sheet_name='Sheet1')
df.to_csv('owid-covid-data.csv')
df = df[df['location'].str.contains("Bahrain", na=False)]
df['date'] = pd.to_datetime(df['date'])
case = df['total_cases']
date = df['date']
fig1 = go.Figure()
fig1.add_trace(
go.Scatter(x=list(date), y=list(case), name='Increment In Case'))
def single_plot(axes, symbol):
days_ago = active_window["days_ago"]
ticks = active_window["ticks"]
alpha = active_window["alpha"]
df = pull(start=n_days_ago(days_ago), symbol=symbol, bin_size=ticks)
df['log_ret'] = np.log(df.close) - np.log(df.close.shift(1))
df["ema_log_ret"] = df.log_ret.ewm(alpha=alpha).mean()
df["ema_log_var"] = df.log_ret.ewm(alpha=alpha).var()
ax1, ax2 = axes
#ax1.plot(df.index, df.log_ret, lw=1, alpha=0.8)
ax1.plot(df.index,
df.ema_log_ret,
lw=1,
alpha=0.8,
label="logreturn %s" % symbol)
ax2.plot(df.index, df.ema_log_var, label="%s variance" % symbol)
n = 200
nm_rolling = 40
# get stock data from yahoo finance
stock = yf.Ticker("GOOG")
dfyh = stock.history(period=f"{n+nm_rolling}d", interval="1d")
dfyh = dfyh.filter(['Close'], axis=1)
dfyh = dfyh.rename(columns={"Close": "goog_close"})
# set timezone to None in dfyh
dfyh.index = dfyh.index.tz_localize(tz=None)
sd = dfyh.index[0:1]
start_date = dfyh.index[0:1].to_pydatetime()[0].astimezone(tz=None)
# get bitcoin/usd data from bitmex
dfbtc = pull(start=start_date, bin_size="1d")
dfbtc.to_pickle("bitmex.pkl")
dfbtc = pd.read_pickle("bitmex.pkl")
# set timezone to None in dfbtc
dfbtc.index = dfbtc.index.tz_localize(tz=None)
dfbtc = dfbtc.filter(['close'], axis=1)
dfbtc = dfbtc.rename(columns={"close": "btc_close"})
df = dfbtc.join(dfyh)
df = df.dropna()
df.btc_close = df.btc_close / df.btc_close.max()
df.goog_close = df.goog_close / df.goog_close.max()
import numpy as np
import pandas as pd
from mplfinance.original_flavor import candlestick_ohlc
from pull_data import n_days_ago, n_hours_ago, pull
#import crosshair
#df = pull(start=n_days_ago(200), bin_size='1d')
#df.to_pickle("bitmex-daily-200d.pkl")
#df = pull(start=n_hours_ago(4))
#df.to_pickle("bitmex.pkl")
symbol = "XBTUSD"
#symbol = "ETHUSD"
df = pull(start=n_days_ago(5), symbol=symbol)
df.to_pickle("bitmex.pkl")
#df = pd.read_pickle("bitmex-daily-200d.pkl")
df = pd.read_pickle("bitmex.pkl")
# bin size = 1 minute
# window = time / bin_size
#window = 5 * 60
#df["sma-5hr"] = df["vwap"].rolling(window=window).mean()
#window = 1 * 24 * 60
#df["sma-1day"] = df["vwap"].rolling(window=window).mean()
#window = 5 * 24 * 60
#df["sma-5day"] = df["vwap"].rolling(window=window).mean()
short_window = 5 * 60
long_window = 1 * 24 * 60
import matplotlib.pyplot as plt
import math
import numpy as np
import pandas as pd
import random
from pull_data import n_days_ago, pull
current_price = 11000
df = pull(start=n_days_ago(60), bin_size="1d")
df.to_pickle("bitmex-daily-60d.pkl")
df = pd.read_pickle("bitmex-daily-60d.pkl")
percent_changes = df["vwap"].pct_change()
log_return = np.log(df.vwap) - np.log(df.vwap.shift(1))
print(log_return)
mean, stddev = log_return.mean(), log_return.std()
print("Mean=%s Stddev=%s" % (mean, stddev))
liquidation_price = 7000
def run_single_simulation(start_price, stop_price):
price = start_price
for i in range(30):
price_log_return = random.normalvariate(mean, stddev)
price = price * math.exp(price_log_return)
if price < stop_price:
return price
return price
import matplotlib.pyplot as plt
import pandas as pd
import time
from pull_data import n_days_ago, n_hours_ago, pull
plt.style.use('dark_background')
days_ago = 10
symbol_perp = "XBTUSD"
df = pull(start=n_days_ago(days_ago), symbol=symbol_perp, bin_size="1m")
df.to_pickle("bitmex.pkl")
df = pd.read_pickle("bitmex.pkl")
#time.sleep(1)
symbol_futures = "XBTZ20"
df2 = pull(start=n_days_ago(days_ago), symbol=symbol_futures, bin_size="1m")
df2.to_pickle("bitmex-%s.pkl" % symbol_futures)
df2 = pd.read_pickle("bitmex-%s.pkl" % symbol_futures)
df = pd.concat([df.close, df2.close], keys=["XBTUSD", "XBTZ20"], axis=1)
df["spread"] = (df.XBTZ20 - df.XBTUSD) / df.XBTUSD
print(df)
print(df.spread.min())
print(df.spread.max())
print(df.spread.mean())
fig, (ax1, ax2) = plt.subplots(2,
sharex=True,
gridspec_kw={
import matplotlib.pyplot as plt
import pandas as pd
from pull_data import n_days_ago, n_hours_ago, pull
# 1. First we fetch the data from the source
# If you've already run this and have the file bitmex.pkl
# then you can comment these lines below and just load straight from disk.
# Pull the data from the exchange
df = pull(start=n_days_ago(5), symbol="XBTUSD")
# Save it to a file
df.to_pickle("bitmex.pkl")
# 2. We load the data
# Load it from the file
df = pd.read_pickle("bitmex.pkl")
# 3. Do some processing...
# 4. Graph the data
plt.style.use('dark_background')
plt.title("BTC spot price")
plt.plot(df.close)
plt.show()
import matplotlib.pyplot as plt
import pandas as pd
from pull_data import n_days_ago, pull
df = pull(start=n_days_ago(10))
df.to_pickle("bitmex.pkl")
df = pd.read_pickle("bitmex.pkl")
lookback = 14 * 60
d_fast_period = 3 * 60
d_slow_period = 3 * 60
#lookback = 48 * 60
#d_fast_period = 3 * 60
#d_slow_period = 6 * 60
lows = df["low"].rolling(lookback).min()
highs = df["high"].rolling(lookback).max()
k = (df["close"] - lows) / (highs - lows)
d_fast = k.rolling(d_fast_period).mean()
d_slow = d_fast.rolling(d_slow_period).mean()
print(lows)
print(highs)
plt.style.use('dark_background')
fig, (ax1, ax2) = plt.subplots(2,
sharex=True,
gridspec_kw={
'hspace': 0,