chore(Linter): reformatted code with black (#211)

* chore(Linter): reformatted code with black

* Create black.yaml

utils/technical_indicators.py

@@ -1,29 +1,43 @@
 import pandas as pd
 import numpy as np
 
+
 def STOK(close, low, high, n):
-    STOK = ((close - low.rolling(n).min()) / (high.rolling(n).max() - low.rolling(n).min())) * 100
+    STOK = (
+        (close - low.rolling(n).min()) / (high.rolling(n).max() - low.rolling(n).min())
+    ) * 100
     return STOK
 
+
 def STOD(close, low, high, n):
-    STOK = ((close - low.rolling(n).min()) / (high.rolling(n).max() - low.rolling(n).min())) * 100
+    STOK = (
+        (close - low.rolling(n).min()) / (high.rolling(n).max() - low.rolling(n).min())
+    ) * 100
     STOD = STOK.rolling(3).mean()
     return STOD
 
+
 def RSI(series, period):
     delta = series.diff().dropna()
-    u=delta*0
+    u = delta * 0
     d = u.copy()
     u[delta > 0] = delta[delta > 0]
     d[delta < 0] = -delta[delta < 0]
-    u[u.index[period-1]] = np.mean( u[:period] ) #first value is sum of avg gains u = u.drop(u.index[:(period-1)])
-    d[d.index[period-1]] = np.mean( d[:period] ) #first value is sum of avg losses d = d.drop(d.index[:(period-1)])
-    rs = u.ewm(com=period-1, adjust=False).mean() / \
-    d.ewm(com=period-1, adjust=False).mean()
-    return 100-100/(1+rs)
+    u[u.index[period - 1]] = np.mean(
+        u[:period]
+    )  # first value is sum of avg gains u = u.drop(u.index[:(period-1)])
+    d[d.index[period - 1]] = np.mean(
+        d[:period]
+    )  # first value is sum of avg losses d = d.drop(d.index[:(period-1)])
+    rs = (
+        u.ewm(com=period - 1, adjust=False).mean()
+        / d.ewm(com=period - 1, adjust=False).mean()
+    )
+    return 100 - 100 / (1 + rs)
+
 
 def ROC(df, n):
     M = df.diff(n - 1)
     N = df.shift(n - 1)
-    ROC = pd.Series(((M / N) * 100), name = 'ROC_' + str(n))
-    return ROC
+    ROC = pd.Series(((M / N) * 100), name="ROC_" + str(n))
+    return ROC