davidzhaodz

def addVerticalBarrier(tEvents, close, numDays=1):
    """
    :param t_events: (series) series of events (symmetric CUSUM filter)
    :param close: (series) close prices
    :param numDays: (int) maximum number of days a trade can be active
     :return: (series) timestamps of vertical barriers
    """
    t1=close.index.searchsorted(tEvents+pd.Timedelta(days=numDays))
    t1=t1[t1<close.shape[0]]
    t1=(pd.Series(close.index[t1],index=tEvents[:t1.shape[0]]))

davidzhaodz

def get_t_events(raw_price, threshold):
    """
    :param raw_price: (series) of close prices.
    :param threshold: (float) when the abs(change) is larger than the threshold, the
    function captures it as an event.
    :return: (datetime index vector) vector of datetimes when the events occurred. This is used later to sample.
    """
    print('Applying Symmetric CUSUM filter.')

    t_events = []

davidzhaodz

def get_daily_vol(close, lookback=100):
    """
    :param close: (data frame) Closing prices
    :param lookback: (int) lookback period to compute volatility
    :return: (series) of daily volatility value
    """
    print('Calculating daily volatility for dynamic thresholds')
    
    df0 = close.index.searchsorted(close.index - pd.Timedelta(days=1))
    df0 = df0[df0 > 0]

davidzhaodz

fig = go.Figure(data=[go.Candlestick(x=df.timestamp,
                     open=df.open, high=df.high,
                     low=df.low, close=df.close)])
fig.show()

davidzhaodz

def get_dollar_bars(time_bars, dollar_threshold): #function credit to Max Bodoia

    # initialize an empty list of dollar bars
    dollar_bars = []

    # initialize the running dollar volume at zero
    running_volume = 0

    # initialize the running high and low with placeholder values
    running_high, running_low = 0, math.inf

davidzhaodz

#create dataframe from lists
df = pd.DataFrame(
    {'time': [datetime.strptime(x,"%Y-%m-%dT%H:%M:%S.%fZ") for x in time], #formatting our timestamp
     'open': [float(x.strip('0')) for x in open], #removing trailing 0's and converting to float
     'high': [float(x.strip('0')) for x in high],
     'low': [float(x.strip('0')) for x in low],
     'close': [float(x.strip('0')) for x in close],
     'vol': [float(x.strip('0')) for x in vol]
    })

davidzhaodz

#collecting historical candlestick data
shrimpy_public_key ='...'
shrimpy_secret_key = '...'
client = shrimpy.ShrimpyApiClient(shrimpy_public_key, shrimpy_secret_key)
candles = client.get_candles(
    'coinbasepro', # exchange
    'BTC',     # base_trading_symbol
    'USD',     # quote_trading_symbol
    '1m'       # interval
)

davidzhaodz

import numpy as np
import pandas as pd
from datetime import datetime, timedelta
import math
import plotly
import shrimpy
import plotly.graph_objects as go
from plotly.subplots import make_subplots

davidzhaodz

#install shrimpy
pip install shrimpy-python

#import requisite packages
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
import math
import plotly
import shrimpy