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

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

#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

#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

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

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_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

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