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Convert to 30 minutes ohlcv data

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Abner Ayala-Acevedo
2017-10-26 15:16:13 -07:00
committed by GitHub
parent cb870422c3
commit c8cc2edd36
1 changed files with 37 additions and 19 deletions
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catalyst/examples/simple_universe.py
+37 -19
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@@ -9,9 +9,13 @@ You will all see how to create a universe and filter it base on the exchange and
The example prints out the closing price of all the pairs for a given market-exchange every 30 minutes.
The example also contains the ohlcv minute data for the past seven days which could be used to create indicators
Use this as the backbone to create your own trading strategies.
Variables lookback date and date are used to ensure data for a coin existed on the lookback period specified.
"""
import numpy as np
import pandas as pd
from datetime import timedelta
from catalyst import run_algorithm
from catalyst.exchange.exchange_utils import get_exchange_symbols
@@ -30,18 +34,16 @@ def handle_data(context, data):
lookback = 60 * 24 * 7 # (minutes, hours, days) of how far to lookback in the data history
context.i += 1
# we must first wait until enough minutes, hours or days have passed for data history to work
if context.i < lookback:
return
# current date formatted into a string
today = context.blotter.current_dt.strftime('%Y-%m-%d %H:%M:%S')
date, time = str(today).split(' ')
today = context.blotter.current_dt
date, time = today.strftime('%Y-%m-%d %H:%M:%S').split(' ')
lookback_date = today - timedelta(days=(lookback / (60 * 24))) # subtract the amount of days specified in lookback
lookback_date = lookback_date.strftime('%Y-%m-%d %H:%M:%S').split(' ')[0] # get only the date as a string
# update universe everyday
new_day = 60 * 24
if not context.i % new_day:
context.universe = universe(context, date)
context.universe = universe(context, lookback_date, date)
# get data every 30 minutes
minutes = 30
@@ -50,25 +52,30 @@ def handle_data(context, data):
for coin in context.coins:
pair = str(coin.symbol)
# ohlcv data (the standard data required for candlestick or indicators/signals)
open = data.history(coin, 'open', bar_count=lookback, frequency='1m').ffill().bfill()
high = data.history(coin, 'high', bar_count=lookback, frequency='1m').ffill().bfill()
low = data.history(coin, 'low', bar_count=lookback, frequency='1m').ffill().bfill()
close = data.history(coin, 'price', bar_count=lookback, frequency='1m').ffill().bfill()
volume = data.history(coin, 'volume', bar_count=lookback, frequency='1m').ffill().bfill()
# 30 minute interval ohlcv data (the standard data required for candlestick or indicators/signals)
# 30T means 30 minutes re-sampling of one minute data. change to your desire time interval.
open = fill(data.history(coin, 'open', bar_count=lookback, frequency='1m')).resample('30T').first()
high = fill(data.history(coin, 'high', bar_count=lookback, frequency='1m')).resample('30T').max()
low = fill(data.history(coin, 'low', bar_count=lookback, frequency='1m')).resample('30T').min()
close = fill(data.history(coin, 'price', bar_count=lookback, frequency='1m')).resample('30T').last()
volume = fill(data.history(coin, 'volume', bar_count=lookback, frequency='1m')).resample('30T').sum()
# close[-1] is the equivalent to current price
# displays the minute price for each pair every 30 minutes
print(today, pair, close[-1])
print(today, pair, open[-1], high[-1], low[-1], close[-1], volume[-1])
# ----------------------------------------------------------------------------------------------------------
# -------------------------------------- Insert Your Strategy Here -----------------------------------------
# ----------------------------------------------------------------------------------------------------------
def analyze(context=None, results=None):
pass
def universe(context, date):
# Get the universe for a given exchange and a given base_currency market
# Example: Poloniex BTC Market
# Get the universe for a given exchange and a given base_currency market
# Example: Poloniex BTC Market
def universe(context, lookback_date, current_date):
json_symbols = get_exchange_symbols(context.exchange) # get all the pairs for the exchange
poloniex_universe_df = pd.DataFrame.from_dict(json_symbols).transpose().astype(str) # convert into a dataframe
poloniex_universe_df['base_currency'] = poloniex_universe_df.apply(lambda row: row.symbol.split('_')[1],
@@ -78,12 +85,23 @@ def universe(context, date):
# Filter all the exchange pairs to only the ones for a give base currency
poloniex_universe_df = poloniex_universe_df[poloniex_universe_df['base_currency'] == context.base_currency]
# Filter all the pairs to ensure that pair existed in the current date
poloniex_universe_df = poloniex_universe_df[poloniex_universe_df.start_date < date]
# Filter all the pairs to ensure that pair existed in the current date range
poloniex_universe_df = poloniex_universe_df[poloniex_universe_df.start_date < lookback_date]
poloniex_universe_df = poloniex_universe_df[poloniex_universe_df.end_daily >= current_date]
context.coins = symbols(*poloniex_universe_df.symbol) # convert all the pairs to symbols
return poloniex_universe_df.symbol.tolist()
# Replace all NA, NAN or infinite values with its nearest value
def fill(series):
if isinstance(series, pd.Series):
return series.replace([np.inf, -np.inf], np.nan).ffill().bfill()
elif isinstance(series, np.ndarray):
return pd.Series(series).replace([np.inf, -np.inf], np.nan).ffill().bfill().values
else:
return series
if __name__ == '__main__':
start_date = pd.to_datetime('2017-01-01', utc=True)
end_date = pd.to_datetime('2017-10-15', utc=True)