Merge pull request #94 from gcarq/autopep

autoformat with autopep8

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

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(),

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),
+        len(results.index), results.profit.mean() * 100.0, results.profit.sum(), results.duration.mean() * 5)
-        results.profit.mean() * 100.0,
-        results.profit.sum(),
-        results.duration.mean() * 5
-    )
 
 
 def print_pair_results(pair, results):
@@ -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):

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 '<code>BTC_ETH\t10.05%</code>' 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={

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'],
+    return pd.DataFrame(
-                                                'high': bars['ha_high'], 'low': bars['ha_low'], 'close': bars['ha_close']})
+        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)