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Merge pull request #18 from vertti/refactor-analyze
Refactor analyze.py
This commit is contained in:
Michael Egger
GitHub
commit
6919166e37
79
analyze.py
79
analyze.py
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@ -13,13 +13,10 @@ logging.basicConfig(level=logging.DEBUG,
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logger = logging.getLogger(__name__)
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def get_ticker_dataframe(pair: str) -> DataFrame:
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def get_ticker(pair: str, minimum_date: arrow.Arrow) -> dict:
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"""
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Analyses the trend for the given pair
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:param pair: pair as str in format BTC_ETH or BTC-ETH
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:return: DataFrame
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Request ticker data from Bittrex for a given currency pair
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"""
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minimum_date = arrow.now() - timedelta(hours=6)
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url = 'https://bittrex.com/Api/v2.0/pub/market/GetTicks'
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headers = {
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'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36',
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@ -32,17 +29,26 @@ def get_ticker_dataframe(pair: str) -> DataFrame:
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data = requests.get(url, params=params, headers=headers).json()
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if not data['success']:
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raise RuntimeError('BITTREX: {}'.format(data['message']))
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return data
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data = [{
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'close': t['C'],
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'volume': t['V'],
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'open': t['O'],
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'high': t['H'],
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'low': t['L'],
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'date': t['T'],
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} for t in sorted(data['result'], key=lambda k: k['T']) if arrow.get(t['T']) > minimum_date]
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dataframe = DataFrame(json_normalize(data))
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def parse_ticker_dataframe(ticker: list, minimum_date: arrow.Arrow) -> DataFrame:
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"""
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Analyses the trend for the given pair
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:param pair: pair as str in format BTC_ETH or BTC-ETH
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:return: DataFrame
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"""
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df = DataFrame(ticker) \
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.drop('BV', 1) \
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.rename(columns={'C':'close', 'V':'volume', 'O':'open', 'H':'high', 'L':'low', 'T':'date'}) \
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.sort_values('date')
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return df[df['date'].map(arrow.get) > minimum_date]
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def populate_indicators(dataframe: DataFrame) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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"""
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dataframe['close_30_ema'] = ta.EMA(dataframe, timeperiod=30)
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dataframe['close_90_ema'] = ta.EMA(dataframe, timeperiod=90)
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@ -60,37 +66,42 @@ def get_ticker_dataframe(pair: str) -> DataFrame:
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return dataframe
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def populate_trends(dataframe: DataFrame) -> DataFrame:
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def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
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"""
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Populates the trends for the given dataframe
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Based on TA indicators, populates the buy trend for the given dataframe
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:param dataframe: DataFrame
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:return: DataFrame with populated trends
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"""
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"""
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dataframe.loc[
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(dataframe['stochrsi'] < 20)
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& (dataframe['close_30_ema'] > (1 + 0.0025) * dataframe['close_60_ema']),
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'underpriced'
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] = 1
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(dataframe['stochrsi'] < 20)
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& (dataframe['macd'] > dataframe['macds'])
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& (dataframe['close'] > dataframe['sar']),
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'underpriced'
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'buy'
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] = 1
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dataframe.loc[dataframe['underpriced'] == 1, 'buy'] = dataframe['close']
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dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
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return dataframe
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def analyze_ticker(pair: str) -> DataFrame:
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"""
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Get ticker data for given currency pair, push it to a DataFrame and
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add several TA indicators and buy signal to it
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:return DataFrame with ticker data and indicator data
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"""
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minimum_date = arrow.utcnow().shift(hours=-6)
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data = get_ticker(pair, minimum_date)
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dataframe = parse_ticker_dataframe(data['result'], minimum_date)
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dataframe = populate_indicators(dataframe)
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dataframe = populate_buy_trend(dataframe)
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return dataframe
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def get_buy_signal(pair: str) -> bool:
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"""
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Calculates a buy signal based on StochRSI indicator
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Calculates a buy signal based several technical analysis indicators
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:param pair: pair in format BTC_ANT or BTC-ANT
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:return: True if pair is underpriced, False otherwise
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:return: True if pair is good for buying, False otherwise
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"""
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dataframe = get_ticker_dataframe(pair)
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dataframe = populate_trends(dataframe)
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dataframe = analyze_ticker(pair)
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latest = dataframe.iloc[-1]
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# Check if dataframe is out of date
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@ -98,7 +109,7 @@ def get_buy_signal(pair: str) -> bool:
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if signal_date < arrow.now() - timedelta(minutes=10):
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return False
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signal = latest['underpriced'] == 1
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signal = latest['buy'] == 1
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logger.debug('buy_trigger: %s (pair=%s, signal=%s)', latest['date'], pair, signal)
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return signal
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@ -123,7 +134,7 @@ def plot_dataframe(dataframe: DataFrame, pair: str) -> None:
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ax1.plot(dataframe.index.values, dataframe['close_30_ema'], label='EMA(30)')
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ax1.plot(dataframe.index.values, dataframe['close_90_ema'], label='EMA(90)')
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# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
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ax1.plot(dataframe.index.values, dataframe['buy'], 'bo', label='buy')
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ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
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ax1.legend()
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ax2.plot(dataframe.index.values, dataframe['macd'], label='MACD')
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@ -150,7 +161,5 @@ if __name__ == '__main__':
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pair = 'BTC_ANT'
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#for pair in ['BTC_ANT', 'BTC_ETH', 'BTC_GNT', 'BTC_ETC']:
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# get_buy_signal(pair)
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dataframe = get_ticker_dataframe(pair)
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dataframe = populate_trends(dataframe)
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plot_dataframe(dataframe, pair)
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plot_dataframe(analyze_ticker(pair), pair)
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time.sleep(60)
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49
test/test_analyze.py
Normal file
49
test/test_analyze.py
Normal file
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@ -0,0 +1,49 @@
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# pragma pylint: disable=missing-docstring
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import unittest
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from unittest.mock import patch
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from pandas import DataFrame
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import arrow
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from analyze import parse_ticker_dataframe, populate_buy_trend, populate_indicators, analyze_ticker, get_buy_signal
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RESULT_BITTREX = {
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'success': True,
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'message': '',
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'result': [
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{'O': 0.00065311, 'H': 0.00065311, 'L': 0.00065311, 'C': 0.00065311, 'V': 22.17210568, 'T': '2017-08-30T10:40:00', 'BV': 0.01448082},
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{'O': 0.00066194, 'H': 0.00066195, 'L': 0.00066194, 'C': 0.00066195, 'V': 33.4727437, 'T': '2017-08-30T10:34:00', 'BV': 0.02215696},
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{'O': 0.00065311, 'H': 0.00065311, 'L': 0.00065311, 'C': 0.00065311, 'V': 53.85127609, 'T': '2017-08-30T10:37:00', 'BV': 0.0351708},
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{'O': 0.00066194, 'H': 0.00066194, 'L': 0.00065311, 'C': 0.00065311, 'V': 46.29210665, 'T': '2017-08-30T10:42:00', 'BV': 0.03063118},
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]
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}
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class TestAnalyze(unittest.TestCase):
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def setUp(self):
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self.result = parse_ticker_dataframe(RESULT_BITTREX['result'], arrow.get('2017-08-30T10:00:00'))
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def test_1_dataframe_has_correct_columns(self):
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self.assertEqual(self.result.columns.tolist(),
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['close', 'high', 'low', 'open', 'date', 'volume'])
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def test_2_orders_by_date(self):
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self.assertEqual(self.result['date'].tolist(),
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['2017-08-30T10:34:00',
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'2017-08-30T10:37:00',
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'2017-08-30T10:40:00',
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'2017-08-30T10:42:00'])
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def test_3_populates_buy_trend(self):
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dataframe = populate_buy_trend(populate_indicators(self.result))
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self.assertTrue('buy' in dataframe.columns)
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self.assertTrue('buy_price' in dataframe.columns)
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def test_4_returns_latest_buy_signal(self):
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buydf = DataFrame([{'buy': 1, 'date': arrow.utcnow()}])
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with patch('analyze.analyze_ticker', return_value=buydf):
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self.assertEqual(get_buy_signal('BTC-ETH'), True)
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buydf = DataFrame([{'buy': 0, 'date': arrow.utcnow()}])
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with patch('analyze.analyze_ticker', return_value=buydf):
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self.assertEqual(get_buy_signal('BTC-ETH'), False)
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if __name__ == '__main__':
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unittest.main()
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