From adfae9e75c6453dd20a2baca280313c7added0e8 Mon Sep 17 00:00:00 2001 From: Janne Sinivirta Date: Mon, 6 Nov 2017 19:01:13 +0200 Subject: [PATCH] autoformat with autopep8 --- freqtrade/analyze.py | 2 +- freqtrade/main.py | 2 +- freqtrade/tests/test_analyze.py | 2 +- freqtrade/tests/test_backtesting.py | 18 +++++------- freqtrade/tests/test_hyperopt.py | 8 +++--- freqtrade/tests/test_telegram.py | 13 +++++++-- freqtrade/vendor/qtpylib/indicators.py | 38 +++++++++++++++----------- 7 files changed, 46 insertions(+), 37 deletions(-) diff --git a/freqtrade/analyze.py b/freqtrade/analyze.py index 3e65cb997..1a9a285fa 100644 --- a/freqtrade/analyze.py +++ b/freqtrade/analyze.py @@ -23,7 +23,7 @@ def parse_ticker_dataframe(ticker: list) -> DataFrame: """ df = DataFrame(ticker) \ .drop('BV', 1) \ - .rename(columns={'C':'close', 'V':'volume', 'O':'open', 'H':'high', 'L':'low', 'T':'date'}) + .rename(columns={'C': 'close', 'V': 'volume', 'O': 'open', 'H': 'high', 'L': 'low', 'T': 'date'}) df['date'] = to_datetime(df['date'], utc=True, infer_datetime_format=True) df.sort_values('date', inplace=True) return df diff --git a/freqtrade/main.py b/freqtrade/main.py index d06204ef7..4c043ef10 100755 --- a/freqtrade/main.py +++ b/freqtrade/main.py @@ -208,7 +208,7 @@ def create_trade(stake_amount: float) -> Optional[Trade]: return Trade(pair=pair, stake_amount=stake_amount, amount=amount, - fee=fee*2, + fee=fee * 2, open_rate=buy_limit, open_date=datetime.utcnow(), exchange=exchange.get_name().upper(), diff --git a/freqtrade/tests/test_analyze.py b/freqtrade/tests/test_analyze.py index 39d166514..3ae61d14f 100644 --- a/freqtrade/tests/test_analyze.py +++ b/freqtrade/tests/test_analyze.py @@ -16,7 +16,7 @@ def result(): def test_dataframe_has_correct_columns(result): assert result.columns.tolist() == \ - ['close', 'high', 'low', 'open', 'date', 'volume'] + ['close', 'high', 'low', 'open', 'date', 'volume'] def test_dataframe_has_correct_length(result): diff --git a/freqtrade/tests/test_backtesting.py b/freqtrade/tests/test_backtesting.py index bf6b81a52..fc7c699d6 100644 --- a/freqtrade/tests/test_backtesting.py +++ b/freqtrade/tests/test_backtesting.py @@ -18,11 +18,7 @@ logging.disable(logging.DEBUG) # disable debug logs that slow backtesting a lot def format_results(results): return 'Made {} buys. Average profit {:.2f}%. Total profit was {:.3f}. Average duration {:.1f} mins.'.format( - len(results.index), - results.profit.mean() * 100.0, - results.profit.sum(), - results.duration.mean() * 5 - ) + len(results.index), results.profit.mean() * 100.0, results.profit.sum(), results.duration.mean() * 5) def print_pair_results(pair, results): @@ -40,10 +36,10 @@ def pairs(): def conf(): return { "minimal_roi": { - "50": 0.0, - "40": 0.01, - "30": 0.02, - "0": 0.045 + "50": 0.0, + "40": 0.01, + "30": 0.02, + "0": 0.045 }, "stoploss": -0.40 } @@ -56,7 +52,7 @@ def backtest(conf, pairs, mocker): mocker.patch.dict('freqtrade.main._CONF', conf) mocker.patch('arrow.utcnow', return_value=arrow.get('2017-08-20T14:50:00')) for pair in pairs: - with open('freqtrade/tests/testdata/'+pair+'.json') as data_file: + with open('freqtrade/tests/testdata/' + pair + '.json') as data_file: mocked_history.return_value = json.load(data_file) ticker = analyze_ticker(pair)[['close', 'date', 'buy']].copy() # for each buy point @@ -65,7 +61,7 @@ def backtest(conf, pairs, mocker): open_rate=row.close, open_date=row.date, amount=1, - fee=exchange.get_fee()*2 + fee=exchange.get_fee() * 2 ) # calculate win/lose forwards from buy point for row2 in ticker[row.Index:].itertuples(index=True): diff --git a/freqtrade/tests/test_hyperopt.py b/freqtrade/tests/test_hyperopt.py index ad2e5564c..5a8143269 100644 --- a/freqtrade/tests/test_hyperopt.py +++ b/freqtrade/tests/test_hyperopt.py @@ -28,10 +28,10 @@ def pairs(): def conf(): return { "minimal_roi": { - "40": 0.0, - "30": 0.01, - "20": 0.02, - "0": 0.04 + "40": 0.0, + "30": 0.01, + "20": 0.02, + "0": 0.04 }, "stoploss": -0.05 } diff --git a/freqtrade/tests/test_telegram.py b/freqtrade/tests/test_telegram.py index 31f44ec55..87e27a668 100644 --- a/freqtrade/tests/test_telegram.py +++ b/freqtrade/tests/test_telegram.py @@ -98,7 +98,11 @@ def test_status_table_handle(conf, update, mocker): mocker.patch.dict('freqtrade.main._CONF', conf) mocker.patch('freqtrade.main.get_buy_signal', side_effect=lambda _: True) msg_mock = MagicMock() - mocker.patch.multiple('freqtrade.main.telegram', _CONF=conf, init=MagicMock(), send_msg=msg_mock) + mocker.patch.multiple( + 'freqtrade.main.telegram', + _CONF=conf, + init=MagicMock(), + send_msg=msg_mock) mocker.patch.multiple('freqtrade.main.exchange', validate_pairs=MagicMock(), get_ticker=MagicMock(return_value={ @@ -269,12 +273,15 @@ def test_performance_handle(conf, update, mocker): assert 'BTC_ETH\t10.05%' in msg_mock.call_args_list[-1][0][0] - def test_count_handle(conf, update, mocker): mocker.patch.dict('freqtrade.main._CONF', conf) mocker.patch('freqtrade.main.get_buy_signal', side_effect=lambda _: True) msg_mock = MagicMock() - mocker.patch.multiple('freqtrade.main.telegram', _CONF=conf, init=MagicMock(), send_msg=msg_mock) + mocker.patch.multiple( + 'freqtrade.main.telegram', + _CONF=conf, + init=MagicMock(), + send_msg=msg_mock) mocker.patch.multiple('freqtrade.main.exchange', validate_pairs=MagicMock(), get_ticker=MagicMock(return_value={ diff --git a/freqtrade/vendor/qtpylib/indicators.py b/freqtrade/vendor/qtpylib/indicators.py index 81e765853..d3c1b89af 100644 --- a/freqtrade/vendor/qtpylib/indicators.py +++ b/freqtrade/vendor/qtpylib/indicators.py @@ -91,7 +91,7 @@ def session(df, start='17:00', end='16:00'): curr = prev = df[-1:].index[0].strftime('%Y-%m-%d') # globex/forex session - if is_same_day == False: + if not is_same_day: prev = (datetime.strptime(curr, '%Y-%m-%d') - timedelta(1)).strftime('%Y-%m-%d') @@ -117,13 +117,19 @@ def heikinashi(bars): bars['ha_high'] = bars.loc[:, ['high', 'ha_open', 'ha_close']].max(axis=1) bars['ha_low'] = bars.loc[:, ['low', 'ha_open', 'ha_close']].min(axis=1) - return pd.DataFrame(index=bars.index, data={'open': bars['ha_open'], - 'high': bars['ha_high'], 'low': bars['ha_low'], 'close': bars['ha_close']}) + return pd.DataFrame( + index=bars.index, + data={ + 'open': bars['ha_open'], + 'high': bars['ha_high'], + 'low': bars['ha_low'], + 'close': bars['ha_close']}) # --------------------------------------------- -def tdi(series, rsi_len=13, bollinger_len=34, rsi_smoothing=2, rsi_signal_len=7, bollinger_std=1.6185): +def tdi(series, rsi_len=13, bollinger_len=34, rsi_smoothing=2, + rsi_signal_len=7, bollinger_std=1.6185): rsi_series = rsi(series, rsi_len) bb_series = bollinger_bands(rsi_series, bollinger_len, bollinger_std) signal = sma(rsi_series, rsi_signal_len) @@ -248,9 +254,9 @@ def rolling_std(series, window=200, min_periods=None): else: try: return series.rolling(window=window, min_periods=min_periods).std() - except: + except BaseException: return pd.Series(series).rolling(window=window, min_periods=min_periods).std() - except: + except BaseException: return pd.rolling_std(series, window=window, min_periods=min_periods) @@ -264,9 +270,9 @@ def rolling_mean(series, window=200, min_periods=None): else: try: return series.rolling(window=window, min_periods=min_periods).mean() - except: + except BaseException: return pd.Series(series).rolling(window=window, min_periods=min_periods).mean() - except: + except BaseException: return pd.rolling_mean(series, window=window, min_periods=min_periods) @@ -277,9 +283,9 @@ def rolling_min(series, window=14, min_periods=None): try: try: return series.rolling(window=window, min_periods=min_periods).min() - except: + except BaseException: return pd.Series(series).rolling(window=window, min_periods=min_periods).min() - except: + except BaseException: return pd.rolling_min(series, window=window, min_periods=min_periods) @@ -290,9 +296,9 @@ def rolling_max(series, window=14, min_periods=None): try: try: return series.rolling(window=window, min_periods=min_periods).min() - except: + except BaseException: return pd.Series(series).rolling(window=window, min_periods=min_periods).min() - except: + except BaseException: return pd.rolling_min(series, window=window, min_periods=min_periods) @@ -302,7 +308,7 @@ def rolling_weighted_mean(series, window=200, min_periods=None): min_periods = window if min_periods is None else min_periods try: return series.ewm(span=window, min_periods=min_periods).mean() - except: + except BaseException: return pd.ewma(series, span=window, min_periods=min_periods) @@ -457,7 +463,7 @@ def returns(series): try: res = (series / series.shift(1) - 1).replace([np.inf, -np.inf], float('NaN')) - except: + except BaseException: res = nans(len(series)) return pd.Series(index=series.index, data=res) @@ -469,7 +475,7 @@ def log_returns(series): try: res = np.log(series / series.shift(1) ).replace([np.inf, -np.inf], float('NaN')) - except: + except BaseException: res = nans(len(series)) return pd.Series(index=series.index, data=res) @@ -482,7 +488,7 @@ def implied_volatility(series, window=252): logret = np.log(series / series.shift(1) ).replace([np.inf, -np.inf], float('NaN')) res = numpy_rolling_std(logret, window) * np.sqrt(window) - except: + except BaseException: res = nans(len(series)) return pd.Series(index=series.index, data=res)