59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
"""
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Functions to analyze ticker data with indicators and produce buy and sell signals
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"""
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import logging
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import pandas as pd
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from pandas import DataFrame, to_datetime
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logger = logging.getLogger(__name__)
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def parse_ticker_dataframe(ticker: list) -> DataFrame:
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"""
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Analyses the trend for the given ticker history
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:param ticker: ticker list, as returned by exchange.async_get_candle_history
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:return: DataFrame
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"""
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cols = ['date', 'open', 'high', 'low', 'close', 'volume']
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frame = DataFrame(ticker, columns=cols)
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frame['date'] = to_datetime(frame['date'],
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unit='ms',
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utc=True,
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infer_datetime_format=True)
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# group by index and aggregate results to eliminate duplicate ticks
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frame = frame.groupby(by='date', as_index=False, sort=True).agg({
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'open': 'first',
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'high': 'max',
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'low': 'min',
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'close': 'last',
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'volume': 'max',
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})
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frame.drop(frame.tail(1).index, inplace=True) # eliminate partial candle
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return frame
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def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
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"""
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Gets order book list, returns dataframe with below format per suggested by creslin
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-------------------------------------------------------------------
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b_sum b_size bids asks a_size a_sum
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-------------------------------------------------------------------
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"""
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cols = ['bids', 'b_size']
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bids_frame = DataFrame(bids, columns=cols)
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# add cumulative sum column
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bids_frame['b_sum'] = bids_frame['b_size'].cumsum()
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cols2 = ['asks', 'a_size']
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asks_frame = DataFrame(asks, columns=cols2)
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# add cumulative sum column
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asks_frame['a_sum'] = asks_frame['a_size'].cumsum()
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frame = pd.concat([bids_frame['b_sum'], bids_frame['b_size'], bids_frame['bids'],
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asks_frame['asks'], asks_frame['a_size'], asks_frame['a_sum']], axis=1,
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keys=['b_sum', 'b_size', 'bids', 'asks', 'a_size', 'a_sum'])
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# logger.info('order book %s', frame )
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return frame
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