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Merge pull request #18 from vertti/refactor-analyze

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Refactor analyze.py
This commit is contained in:
Michael Egger 2017-09-10 22:37:30 +02:00 committed by GitHub
commit 6919166e37
2 changed files with 94 additions and 36 deletions
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81
analyze.py
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@ -13,13 +13,10 @@ logging.basicConfig(level=logging.DEBUG,
logger = logging.getLogger(__name__)
def get_ticker_dataframe(pair: str) -> DataFrame:
def get_ticker(pair: str, minimum_date: arrow.Arrow) -> dict:
"""
Analyses the trend for the given pair
:param pair: pair as str in format BTC_ETH or BTC-ETH
:return: DataFrame
Request ticker data from Bittrex for a given currency pair
"""
minimum_date = arrow.now() - timedelta(hours=6)
url = 'https://bittrex.com/Api/v2.0/pub/market/GetTicks'
headers = {
'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',
@ -32,17 +29,26 @@ def get_ticker_dataframe(pair: str) -> DataFrame:
data = requests.get(url, params=params, headers=headers).json()
if not data['success']:
raise RuntimeError('BITTREX: {}'.format(data['message']))
return data
data = [{
'close': t['C'],
'volume': t['V'],
'open': t['O'],
'high': t['H'],
'low': t['L'],
'date': t['T'],
} for t in sorted(data['result'], key=lambda k: k['T']) if arrow.get(t['T']) > minimum_date]
dataframe = DataFrame(json_normalize(data))
def parse_ticker_dataframe(ticker: list, minimum_date: arrow.Arrow) -> DataFrame:
"""
Analyses the trend for the given pair
:param pair: pair as str in format BTC_ETH or BTC-ETH
:return: DataFrame
"""
df = DataFrame(ticker) \
.drop('BV', 1) \
.rename(columns={'C':'close', 'V':'volume', 'O':'open', 'H':'high', 'L':'low', 'T':'date'}) \
.sort_values('date')
return df[df['date'].map(arrow.get) > minimum_date]
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['close_30_ema'] = ta.EMA(dataframe, timeperiod=30)
dataframe['close_90_ema'] = ta.EMA(dataframe, timeperiod=90)
@ -60,37 +66,42 @@ def get_ticker_dataframe(pair: str) -> DataFrame:
return dataframe
def populate_trends(dataframe: DataFrame) -> DataFrame:
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
"""
Populates the trends for the given dataframe
Based on TA indicators, populates the buy trend for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with populated trends
"""
:return: DataFrame with buy column
"""
dataframe.loc[
(dataframe['stochrsi'] < 20)
& (dataframe['close_30_ema'] > (1 + 0.0025) * dataframe['close_60_ema']),
'underpriced'
] = 1
"""
dataframe.loc[
(dataframe['stochrsi'] < 20)
& (dataframe['macd'] > dataframe['macds'])
& (dataframe['macd'] > dataframe['macds'])
& (dataframe['close'] > dataframe['sar']),
'underpriced'
'buy'
] = 1
dataframe.loc[dataframe['underpriced'] == 1, 'buy'] = dataframe['close']
dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
return dataframe
def analyze_ticker(pair: str) -> DataFrame:
"""
Get ticker data for given currency pair, push it to a DataFrame and
add several TA indicators and buy signal to it
:return DataFrame with ticker data and indicator data
"""
minimum_date = arrow.utcnow().shift(hours=-6)
data = get_ticker(pair, minimum_date)
dataframe = parse_ticker_dataframe(data['result'], minimum_date)
dataframe = populate_indicators(dataframe)
dataframe = populate_buy_trend(dataframe)
return dataframe
def get_buy_signal(pair: str) -> bool:
"""
Calculates a buy signal based on StochRSI indicator
Calculates a buy signal based several technical analysis indicators
:param pair: pair in format BTC_ANT or BTC-ANT
:return: True if pair is underpriced, False otherwise
:return: True if pair is good for buying, False otherwise
"""
dataframe = get_ticker_dataframe(pair)
dataframe = populate_trends(dataframe)
dataframe = analyze_ticker(pair)
latest = dataframe.iloc[-1]
# Check if dataframe is out of date
@ -98,7 +109,7 @@ def get_buy_signal(pair: str) -> bool:
if signal_date < arrow.now() - timedelta(minutes=10):
return False
signal = latest['underpriced'] == 1
signal = latest['buy'] == 1
logger.debug('buy_trigger: %s (pair=%s, signal=%s)', latest['date'], pair, signal)
return signal
@ -123,7 +134,7 @@ def plot_dataframe(dataframe: DataFrame, pair: str) -> None:
ax1.plot(dataframe.index.values, dataframe['close_30_ema'], label='EMA(30)')
ax1.plot(dataframe.index.values, dataframe['close_90_ema'], label='EMA(90)')
# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
ax1.plot(dataframe.index.values, dataframe['buy'], 'bo', label='buy')
ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
ax1.legend()
ax2.plot(dataframe.index.values, dataframe['macd'], label='MACD')
@ -150,7 +161,5 @@ if __name__ == '__main__':
pair = 'BTC_ANT'
#for pair in ['BTC_ANT', 'BTC_ETH', 'BTC_GNT', 'BTC_ETC']:
# get_buy_signal(pair)
dataframe = get_ticker_dataframe(pair)
dataframe = populate_trends(dataframe)
plot_dataframe(dataframe, pair)
plot_dataframe(analyze_ticker(pair), pair)
time.sleep(60)

49
test/test_analyze.py Normal file
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@ -0,0 +1,49 @@
# pragma pylint: disable=missing-docstring
import unittest
from unittest.mock import patch
from pandas import DataFrame
import arrow
from analyze import parse_ticker_dataframe, populate_buy_trend, populate_indicators, analyze_ticker, get_buy_signal
RESULT_BITTREX = {
'success': True,
'message': '',
'result': [
{'O': 0.00065311, 'H': 0.00065311, 'L': 0.00065311, 'C': 0.00065311, 'V': 22.17210568, 'T': '2017-08-30T10:40:00', 'BV': 0.01448082},
{'O': 0.00066194, 'H': 0.00066195, 'L': 0.00066194, 'C': 0.00066195, 'V': 33.4727437, 'T': '2017-08-30T10:34:00', 'BV': 0.02215696},
{'O': 0.00065311, 'H': 0.00065311, 'L': 0.00065311, 'C': 0.00065311, 'V': 53.85127609, 'T': '2017-08-30T10:37:00', 'BV': 0.0351708},
{'O': 0.00066194, 'H': 0.00066194, 'L': 0.00065311, 'C': 0.00065311, 'V': 46.29210665, 'T': '2017-08-30T10:42:00', 'BV': 0.03063118},
]
}
class TestAnalyze(unittest.TestCase):
def setUp(self):
self.result = parse_ticker_dataframe(RESULT_BITTREX['result'], arrow.get('2017-08-30T10:00:00'))
def test_1_dataframe_has_correct_columns(self):
self.assertEqual(self.result.columns.tolist(),
['close', 'high', 'low', 'open', 'date', 'volume'])
def test_2_orders_by_date(self):
self.assertEqual(self.result['date'].tolist(),
['2017-08-30T10:34:00',
'2017-08-30T10:37:00',
'2017-08-30T10:40:00',
'2017-08-30T10:42:00'])
def test_3_populates_buy_trend(self):
dataframe = populate_buy_trend(populate_indicators(self.result))
self.assertTrue('buy' in dataframe.columns)
self.assertTrue('buy_price' in dataframe.columns)
def test_4_returns_latest_buy_signal(self):
buydf = DataFrame([{'buy': 1, 'date': arrow.utcnow()}])
with patch('analyze.analyze_ticker', return_value=buydf):
self.assertEqual(get_buy_signal('BTC-ETH'), True)
buydf = DataFrame([{'buy': 0, 'date': arrow.utcnow()}])
with patch('analyze.analyze_ticker', return_value=buydf):
self.assertEqual(get_buy_signal('BTC-ETH'), False)
if __name__ == '__main__':
unittest.main()