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Merge branch 'develop' of github.com:enigmampc/catalyst into develop
This commit is contained in:
Victor Grau Serrat
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"""
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Requires Catalyst version 0.3.0 or above
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Tested on Catalyst version 0.3.3
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These example aims to provide and easy way for users to learn how to collect data from the different exchanges.
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You simply need to specify the exchange and the market that you want to focus on.
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You will all see how to create a universe and filter it base on the exchange and the market you desire.
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The example prints out the closing price of all the pairs for a given market-exchange every 30 minutes.
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The example also contains the ohlcv minute data for the past seven days which could be used to create indicators
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Use this as the backbone to create your own trading strategies.
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Variables lookback date and date are used to ensure data for a coin existed on the lookback period specified.
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"""
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import numpy as np
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import pandas as pd
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from datetime import timedelta
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from catalyst import run_algorithm
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from catalyst.exchange.exchange_utils import get_exchange_symbols
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from catalyst.api import (
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symbols,
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)
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def initialize(context):
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context.i = -1 # counts the minutes
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context.exchange = context.exchanges.values()[0].name.lower() # exchange name
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context.base_currency = context.exchanges.values()[0].base_currency.lower() # market base currency
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def handle_data(context, data):
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context.i += 1
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lookback_days = 7 # 7 days
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# current date formatted into a string
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today = data.current_dt
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date, time = today.strftime('%Y-%m-%d %H:%M:%S').split(' ')
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lookback_date = today - timedelta(days=lookback_days) # subtract the amount of days specified in lookback
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lookback_date = lookback_date.strftime('%Y-%m-%d %H:%M:%S').split(' ')[0] # get only the date as a string
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# update universe everyday
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new_day = 60 * 24 # assuming data_frequency='minute'
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if not context.i % new_day:
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context.universe = universe(context, lookback_date, date)
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# get data every 30 minutes
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minutes = 30
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one_day_in_minutes = 1440 # 1440 assumes data_frequency='minute'
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lookback = one_day_in_minutes / minutes * lookback_days # get N lookback_days of history data
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if not context.i % minutes and context.universe:
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# we iterate for every pair in the current universe
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for coin in context.coins:
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pair = str(coin.symbol)
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# 30 minute interval ohlcv data (the standard data required for candlestick or indicators/signals)
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# 30T means 30 minutes re-sampling of one minute data. change to your desire time interval.
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opened = fill(data.history(coin, 'open', bar_count=lookback, frequency='30T')).values
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high = fill(data.history(coin, 'high', bar_count=lookback, frequency='30T')).values
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low = fill(data.history(coin, 'low', bar_count=lookback, frequency='30T')).values
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close = fill(data.history(coin, 'price', bar_count=lookback, frequency='30T')).values
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volume = fill(data.history(coin, 'volume', bar_count=lookback, frequency='30T')).values
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# close[-1] is the equivalent to current price
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# displays the minute price for each pair every 30 minutes
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print(today, pair, opened[-1], high[-1], low[-1], close[-1], volume[-1])
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# ----------------------------------------------------------------------------------------------------------
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# -------------------------------------- Insert Your Strategy Here -----------------------------------------
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# ----------------------------------------------------------------------------------------------------------
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def analyze(context=None, results=None):
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pass
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# Get the universe for a given exchange and a given base_currency market
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# Example: Poloniex BTC Market
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def universe(context, lookback_date, current_date):
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json_symbols = get_exchange_symbols(context.exchange) # get all the pairs for the exchange
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universe_df = pd.DataFrame.from_dict(json_symbols).transpose().astype(str) # convert into a dataframe
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universe_df['base_currency'] = universe_df.apply(lambda row: row.symbol.split('_')[1],
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axis=1)
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universe_df['market_currency'] = universe_df.apply(lambda row: row.symbol.split('_')[0],
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axis=1)
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# Filter all the exchange pairs to only the ones for a give base currency
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universe_df = universe_df[universe_df['base_currency'] == context.base_currency]
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# Filter all the pairs to ensure that pair existed in the current date range
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universe_df = universe_df[universe_df.start_date < lookback_date]
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universe_df = universe_df[universe_df.end_daily >= current_date]
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context.coins = symbols(*universe_df.symbol) # convert all the pairs to symbols
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# print(universe_df.symbol.tolist())
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return universe_df.symbol.tolist()
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# Replace all NA, NAN or infinite values with its nearest value
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def fill(series):
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if isinstance(series, pd.Series):
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return series.replace([np.inf, -np.inf], np.nan).ffill().bfill()
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elif isinstance(series, np.ndarray):
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return pd.Series(series).replace([np.inf, -np.inf], np.nan).ffill().bfill().values
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else:
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return series
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if __name__ == '__main__':
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start_date = pd.to_datetime('2017-01-01', utc=True)
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end_date = pd.to_datetime('2017-11-13', utc=True)
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performance = run_algorithm(start=start_date, end=end_date,
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capital_base=100.0, # amount of base_currency, not always in dollars unless usd
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initialize=initialize,
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handle_data=handle_data,
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analyze=analyze,
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exchange_name='poloniex',
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data_frequency='minute',
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base_currency='btc',
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live=False,
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live_graph=False,
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algo_namespace='simple_universe')
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"""
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Run in Terminal (inside catalyst environment):
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python simple_universe.py
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"""
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@@ -226,10 +226,9 @@ class Poloniex(Exchange):
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ohlc_map = dict()
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for asset in asset_list:
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delta = end_dt - pd.to_datetime('1970-1-1', utc=True)
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end = int(delta.total_seconds())
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# TODO: what's wrong with this?
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# end = int(time.mktime(end_dt.timetuple()))
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end = int(time.time())
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if bar_count is None:
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start = end - 2 * frequency
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else:
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@@ -113,3 +113,40 @@ class TestExchangeDataPortal:
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)
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log.info('found history window: {}'.format(data))
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def test_validate_resample(self):
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symbol = ['eth_btc']
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exchange_name = 'poloniex'
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exchange = get_exchange(exchange_name, base_currency=symbol)
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assets = exchange.get_assets(symbols=symbol)
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date = rnd_history_date_days(
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max_days=10,
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last_dt=pd.to_datetime('2017-11-1', utc=True)
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)
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bar_count = rnd_bar_count(max_bars=10)
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sample_minutes = 15
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sample_data = self.data_portal_backtest.get_history_window(
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assets=assets,
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end_dt=date,
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bar_count=bar_count,
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frequency='{}T'.format(sample_minutes),
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field='close',
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data_frequency='daily'
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)
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minute_data = self.data_portal_backtest.get_history_window(
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assets=assets,
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end_dt=date,
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bar_count=bar_count * sample_minutes,
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frequency='1T',
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field='close',
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data_frequency='daily'
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)
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resampled_minute_data = minute_data.resample(
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'{}T'.format(sample_minutes))
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print(sample_data.tail(10))
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print(resampled_minute_data.tail(10))
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print(minute_data.tail(10))
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pass
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@@ -1,9 +1,10 @@
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from catalyst.exchange.bittrex.bittrex import Bittrex
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from catalyst.exchange.poloniex.poloniex import Poloniex
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from catalyst.finance.order import Order
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from base import BaseExchangeTestCase
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from logbook import Logger
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from catalyst.exchange.exchange_utils import get_exchange_auth
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import pandas as pd
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from test_utils import output_df
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log = Logger('test_poloniex')
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@@ -51,18 +52,19 @@ class TestPoloniex(BaseExchangeTestCase):
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def test_get_candles(self):
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log.info('retrieving candles')
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ohlcv_neo = self.exchange.get_candles(
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freq='5T',
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assets=self.exchange.get_asset('eth_btc')
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)
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ohlcv_neo_ubq = self.exchange.get_candles(
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freq='5T',
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assets=[
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self.exchange.get_asset('neos_btc'),
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self.exchange.get_asset('via_btc')
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],
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bar_count=14
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assets = self.exchange.get_asset('eth_btc')
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ohlcv = self.exchange.get_candles(
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end_dt=pd.to_datetime('2017-11-01', utc=True),
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freq='30T',
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assets=assets,
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bar_count=200
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)
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df = pd.DataFrame(ohlcv)
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df.set_index('last_traded', drop=True, inplace=True)
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log.info(df.tail(25))
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path = output_df(df, assets, 'candles')
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log.info('saved candles: {}'.format(path))
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pass
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def test_tickers(self):
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@@ -4,11 +4,20 @@ from random import randint
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import pandas as pd
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def rnd_history_date_days(max_days=30):
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now = pd.Timestamp.utcnow()
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def rnd_history_date_days(max_days=30, last_dt=None):
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if last_dt is None:
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last_dt = pd.Timestamp.utcnow()
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days = randint(0, max_days)
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return now - timedelta(days=days)
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return last_dt - timedelta(days=days)
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def rnd_history_date_minutes(max_minutes=1440):
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now = pd.Timestamp.utcnow()
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days = randint(0, max_minutes)
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return now - timedelta(minutes=days)
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def rnd_bar_count(max_bars=21):
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