def pivots_points(dataframe: pd.DataFrame,
timeperiod=30,
levels=3) -> pd.DataFrame:
"""
Pivots Points
https://www.tradingview.com/support/solutions/43000521824-pivot-points-standard/
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)
def pivots_points(dataframe: pd.DataFrame,
timeperiod=1,
levels=4) -> pd.DataFrame:
"""
Pivots Points
https://www.tradingview.com/support/solutions/43000521824-pivot-points-standard/
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 ... Standard
# R1 = PP + 0.382 * (HIGHprev - LOWprev) ... fibonacci
data["r1"] = data['pivot'] + 0.382 * (high - low)
data["rS1"] = data['pivot'] + 0.0955 * (high - low)
# Resistance #2
# data["s1"] = (2 * data["pivot"]) - high ... Standard
# S1 = PP - 0.382 * (HIGHprev - LOWprev) ... fibonacci
data["s1"] = data["pivot"] - 0.382 * (high - low)
# 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)
def pivot_points(dataframe: pd.DataFrame, timeperiod=30) -> pd.DataFrame:
"""
Pivot Points Camarilla
https://www.tradingview.com/support/solutions/43000521824-pivot-points-standard/
Formula:
Pivot = (Previous High + Previous Low + Previous Close)/3
Resistance #1 = Previous Close + 1.1 * (Previous High - Previous Low)/12
Support #1 = Previous Close - 1.1 * (Previous High - Previous Low)/12
Resistance #2 = Previous Close + 1.1 * (Previous High - Previous Low)/6
Support #2 = Previous Close - 1.1 * (Previous High - Previous Low)/6
Resistance #3 = Previous Close + 1.1 * (Previous High - Previous Low)/4
Support #3 = Previous Close - 1.1 * (Previous High - Previous Low)/4
Resistance #4 = Previous Close + 1.1 * (Previous High - Previous Low)/2
Support #4 = Previous Close - 1.1 * (Previous High - Previous Low)/2
:param dataframe:
:param timeperiod: Period to compare (in ticker)
: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)
close = qtpylib.rolling_mean(series=pd.Series(index=dataframe.index,
data=dataframe['close']),
window=timeperiod)
# Pivot
data['pivot'] = qtpylib.rolling_mean(
series=qtpylib.typical_price(dataframe), window=timeperiod)
price_range = high - low
resistence = lambda x: close + 1.1 * (price_range) / x
support = lambda x: close - 1.1 * (price_range) / x
# Support and Resistance #1
data['r1'] = resistence(12)
data['s1'] = support(12)
# Support and Resistance #2
data['r2'] = resistence(6)
data['s2'] = support(6)
# Support and Resistance #3
data['r3'] = resistence(4)
data['s3'] = support(4)
# Support and Resistance #4
data['r4'] = resistence(2)
data['s4'] = support(2)
return pd.DataFrame(index=dataframe.index, data=data)
