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flake happy

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hroff-1902 2019-05-03 16:58:51 +03:00
parent 1be4c59481
commit 66c2bdd65a
1 changed files with 16 additions and 12 deletions
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28
freqtrade/vendor/qtpylib/indicators.py vendored
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@ -260,7 +260,7 @@ def rolling_std(series, window=200, min_periods=None):
else: else:
try: try:
return series.rolling(window=window, min_periods=min_periods).std() return series.rolling(window=window, min_periods=min_periods).std()
except Exception as e: except Exception as e: # noqa: F841
return pd.Series(series).rolling(window=window, min_periods=min_periods).std() return pd.Series(series).rolling(window=window, min_periods=min_periods).std()
# --------------------------------------------- # ---------------------------------------------
@ -273,7 +273,7 @@ def rolling_mean(series, window=200, min_periods=None):
else: else:
try: try:
return series.rolling(window=window, min_periods=min_periods).mean() return series.rolling(window=window, min_periods=min_periods).mean()
except Exception as e: except Exception as e: # noqa: F841
return pd.Series(series).rolling(window=window, min_periods=min_periods).mean() return pd.Series(series).rolling(window=window, min_periods=min_periods).mean()
# --------------------------------------------- # ---------------------------------------------
@ -283,7 +283,7 @@ def rolling_min(series, window=14, min_periods=None):
min_periods = window if min_periods is None else min_periods min_periods = window if min_periods is None else min_periods
try: try:
return series.rolling(window=window, min_periods=min_periods).min() return series.rolling(window=window, min_periods=min_periods).min()
except Exception as e: except Exception as e: # noqa: F841
return pd.Series(series).rolling(window=window, min_periods=min_periods).min() return pd.Series(series).rolling(window=window, min_periods=min_periods).min()
@ -293,7 +293,7 @@ def rolling_max(series, window=14, min_periods=None):
min_periods = window if min_periods is None else min_periods min_periods = window if min_periods is None else min_periods
try: try:
return series.rolling(window=window, min_periods=min_periods).min() return series.rolling(window=window, min_periods=min_periods).min()
except Exception as e: except Exception as e: # noqa: F841
return pd.Series(series).rolling(window=window, min_periods=min_periods).min() return pd.Series(series).rolling(window=window, min_periods=min_periods).min()
@ -303,7 +303,7 @@ def rolling_weighted_mean(series, window=200, min_periods=None):
min_periods = window if min_periods is None else min_periods min_periods = window if min_periods is None else min_periods
try: try:
return series.ewm(span=window, min_periods=min_periods).mean() return series.ewm(span=window, min_periods=min_periods).mean()
except Exception as e: except Exception as e: # noqa: F841
return pd.ewma(series, span=window, min_periods=min_periods) return pd.ewma(series, span=window, min_periods=min_periods)
@ -366,7 +366,8 @@ def rolling_vwap(bars, window=200, min_periods=None):
min_periods=min_periods).sum() min_periods=min_periods).sum()
right = volume.rolling(window=window, min_periods=min_periods).sum() right = volume.rolling(window=window, min_periods=min_periods).sum()
return pd.Series(index=bars.index, data=(left / right)).replace([np.inf, -np.inf], float('NaN')).ffill() return pd.Series(index=bars.index, data=(left / right)
).replace([np.inf, -np.inf], float('NaN')).ffill()
# --------------------------------------------- # ---------------------------------------------
@ -460,7 +461,7 @@ def returns(series):
try: try:
res = (series / series.shift(1) - res = (series / series.shift(1) -
1).replace([np.inf, -np.inf], float('NaN')) 1).replace([np.inf, -np.inf], float('NaN'))
except Exception as e: except Exception as e: # noqa: F841
res = nans(len(series)) res = nans(len(series))
return pd.Series(index=series.index, data=res) return pd.Series(index=series.index, data=res)
@ -471,8 +472,8 @@ def returns(series):
def log_returns(series): def log_returns(series):
try: try:
res = np.log(series / series.shift(1) res = np.log(series / series.shift(1)
).replace([np.inf, -np.inf], float('NaN')) ).replace([np.inf, -np.inf], float('NaN'))
except Exception as e: except Exception as e: # noqa: F841
res = nans(len(series)) res = nans(len(series))
return pd.Series(index=series.index, data=res) return pd.Series(index=series.index, data=res)
@ -483,9 +484,9 @@ def log_returns(series):
def implied_volatility(series, window=252): def implied_volatility(series, window=252):
try: try:
logret = np.log(series / series.shift(1) logret = np.log(series / series.shift(1)
).replace([np.inf, -np.inf], float('NaN')) ).replace([np.inf, -np.inf], float('NaN'))
res = numpy_rolling_std(logret, window) * np.sqrt(window) res = numpy_rolling_std(logret, window) * np.sqrt(window)
except Exception as e: except Exception as e: # noqa: F841
res = nans(len(series)) res = nans(len(series))
return pd.Series(index=series.index, data=res) return pd.Series(index=series.index, data=res)
@ -542,7 +543,10 @@ def stoch(df, window=14, d=3, k=3, fast=False):
my_df['rolling_max'] = df['high'].rolling(window).max() my_df['rolling_max'] = df['high'].rolling(window).max()
my_df['rolling_min'] = df['low'].rolling(window).min() my_df['rolling_min'] = df['low'].rolling(window).min()
my_df['fast_k'] = 100 * (df['close'] - my_df['rolling_min'])/(my_df['rolling_max'] - my_df['rolling_min']) my_df['fast_k'] = (
100 * (df['close'] - my_df['rolling_min']) /
(my_df['rolling_max'] - my_df['rolling_min'])
)
my_df['fast_d'] = my_df['fast_k'].rolling(d).mean() my_df['fast_d'] = my_df['fast_k'].rolling(d).mean()
if fast: if fast: