diff --git a/analyze.py b/analyze.py index 6ac798757..4878333b6 100644 --- a/analyze.py +++ b/analyze.py @@ -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) diff --git a/test/test_analyze.py b/test/test_analyze.py new file mode 100644 index 000000000..9fdc16d7a --- /dev/null +++ b/test/test_analyze.py @@ -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()