mirror of
https://github.com/wassname/catalyst.git
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Frederic Fortier
121 lines
3.4 KiB
Python
121 lines
3.4 KiB
Python
import importlib
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import os
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import matplotlib
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import matplotlib.pyplot as plt
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# from matplotlib.finance import volume_overlay
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import matplotlib.ticker as ticker
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import pandas as pd
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from matplotlib.finance import candlestick2_ohlc
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from catalyst.exchange.exchange_bcolz import BcolzExchangeBarReader
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from catalyst.exchange.exchange_bundle import ExchangeBundle
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from catalyst.exchange.utils.bundle_utils import get_df_from_arrays, \
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get_bcolz_chunk
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from catalyst.exchange.utils.factory import get_exchange
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EXCHANGE_NAMES = ['bitfinex', 'bittrex', 'poloniex']
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exchanges = dict((e, getattr(importlib.import_module(
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'catalyst.exchange.{0}.{0}'.format(e)), e.capitalize()))
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for e in EXCHANGE_NAMES)
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class ValidateChunks(object):
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def __init__(self):
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self.columns = ['open', 'high', 'low', 'close', 'volume']
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def chunk_to_df(self, exchange_name, symbol, data_frequency, period):
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exchange = get_exchange(exchange_name)
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asset = exchange.get_asset(symbol)
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filename = get_bcolz_chunk(
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exchange_name=exchange_name,
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symbol=symbol,
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data_frequency=data_frequency,
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period=period
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)
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reader = BcolzExchangeBarReader(rootdir=filename,
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data_frequency=data_frequency)
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# metadata = BcolzMinuteBarMetadata.read(filename)
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start = reader.first_trading_day
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end = reader.last_available_dt
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if data_frequency == 'daily':
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end = end - pd.Timedelta(hours=23, minutes=59)
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print(start, end, data_frequency)
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arrays = reader.load_raw_arrays(self.columns, start, end,
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[asset.sid, ])
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bundle = ExchangeBundle(exchange_name)
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periods = bundle.get_calendar_periods_range(
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start, end, data_frequency
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)
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return get_df_from_arrays(arrays, periods)
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def plot_ohlcv(self, df):
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fig, ax = plt.subplots()
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# Plot the candlestick
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candlestick2_ohlc(ax, df['open'], df['high'], df['low'], df['close'],
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width=1, colorup='g', colordown='r', alpha=0.5)
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# shift y-limits of the candlestick plot so that there is space
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# at the bottom for the volume bar chart
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pad = 0.25
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yl = ax.get_ylim()
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ax.set_ylim(yl[0] - (yl[1] - yl[0]) * pad, yl[1])
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# Add a seconds axis for the volume overlay
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ax2 = ax.twinx()
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ax2.set_position(
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matplotlib.transforms.Bbox([[0.125, 0.1], [0.9, 0.26]]))
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# Plot the volume overlay
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# bc = volume_overlay(ax2, df['open'], df['close'], df['volume'],
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# colorup='g', alpha=0.5, width=1)
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ax.xaxis.set_major_locator(ticker.MaxNLocator(6))
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def mydate(x, pos):
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try:
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return df.index[int(x)]
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except IndexError:
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return ''
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ax.xaxis.set_major_formatter(ticker.FuncFormatter(mydate))
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plt.margins(0)
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plt.show()
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def plot(self, filename):
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df = self.chunk_to_df(filename)
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self.plot_ohlcv(df)
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def to_csv(self, filename):
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df = self.chunk_to_df(filename)
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df.to_csv(os.path.basename(filename).split('.')[0] + '.csv')
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v = ValidateChunks()
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df = v.chunk_to_df(
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exchange_name='bitfinex',
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symbol='eth_btc',
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data_frequency='daily',
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period='2016'
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)
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print(df.tail())
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v.plot_ohlcv(df)
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# v.plot(
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# ex
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# )
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