From a9b1cc507cc55bd9a4073ab7630b3449af1b58af Mon Sep 17 00:00:00 2001
From: hroff-1902 <hroff_raeyd9tzpttf@mail.ru>
Date: Sat, 10 Aug 2019 16:48:24 +0300
Subject: [PATCH] indicators removed as it was moved to technical repo

---
 user_data/indicators/indicators.py | 79 ------------------------------
 1 file changed, 79 deletions(-)
 delete mode 100644 user_data/indicators/indicators.py

diff --git a/user_data/indicators/indicators.py b/user_data/indicators/indicators.py
deleted file mode 100644
index 8d5a369..0000000
--- a/user_data/indicators/indicators.py
+++ /dev/null
@@ -1,79 +0,0 @@
-
-import freqtrade.vendor.qtpylib.indicators as qtpylib
-import pandas as pd
-
-"""
-Indicators for Freqtrade
-author@: Gerald Lonlas
-github@: https://github.com/freqtrade/freqtrade-strategies
-"""
-
-def pivots_points(dataframe: pd.DataFrame, timeperiod=30, levels=3) -> pd.DataFrame:
-    """
-    Pivots Points
-
-    Formula:
-    Pivot = (Previous High + Previous Low + Previous Close)/3
-
-    Resistance #1 = (2 x Pivot) - Previous Low
-    Support #1 = (2 x Pivot) - Previous High
-
-    Resistance #2 = (Pivot - Support #1) + Resistance #1
-    Support #2 = Pivot - (Resistance #1 - Support #1)
-
-    Resistance #3 = (Pivot - Support #2) + Resistance #2
-    Support #3 = Pivot - (Resistance #2 - Support #2)
-    ...
-
-    :param dataframe:
-    :param timeperiod: Period to compare (in ticker)
-    :param levels: Num of support/resistance desired
-    :return: dataframe
-    """
-
-    data = {}
-
-    low = qtpylib.rolling_mean(
-        series=pd.Series(
-            index=dataframe.index,
-            data=dataframe['low']
-        ),
-        window=timeperiod
-    )
-
-    high = qtpylib.rolling_mean(
-        series=pd.Series(
-            index=dataframe.index,
-            data=dataframe['high']
-        ),
-        window=timeperiod
-    )
-
-    # Pivot
-    data['pivot'] = qtpylib.rolling_mean(
-        series=qtpylib.typical_price(dataframe),
-        window=timeperiod
-    )
-
-    # Resistance #1
-    data['r1'] = (2 * data['pivot']) - low
-
-    # Resistance #2
-    data['s1'] = (2 * data['pivot']) - high
-
-    # Calculate Resistances and Supports >1
-    for i in range(2, levels+1):
-        prev_support = data['s' + str(i - 1)]
-        prev_resistance = data['r' + str(i - 1)]
-
-        # Resitance
-        data['r'+ str(i)] = (data['pivot'] - prev_support) + prev_resistance
-
-        # Support
-        data['s' + str(i)] = data['pivot'] - (prev_resistance - prev_support)
-
-    return pd.DataFrame(
-        index=dataframe.index,
-        data=data
-    )
-