def triple_moving_average(fund: pd.DataFrame, **kwargs) -> dict:
"""Triple Moving Average
3 simple moving averages of "config" length
Arguments:
fund {pd.DataFrame} -- fund historical data
Optional Args:
name {list} -- name of fund, primarily for plotting (default: {''})
plot_output {bool} -- True to render plot in realtime (default: {True})
config {list of ints} -- list of moving average time periods (default: {[12, 50, 200]})
progress_bar {ProgressBar} -- (default: {None})
view {str} -- directory of plots (default: {''})
Returns:
tma {dict} -- contains all ma information in "short", "medium", and "long" keys
"""
name = kwargs.get('name', '')
config = kwargs.get('config', [12, 50, 200])
plot_output = kwargs.get('plot_output', True)
out_suppress = kwargs.get('out_suppress', False)
progress_bar = kwargs.get('progress_bar', None)
view = kwargs.get('view', '')
tshort = simple_moving_avg(fund, config[0])
if progress_bar is not None:
progress_bar.uptick(increment=0.2)
tmed = simple_moving_avg(fund, config[1])
if progress_bar is not None:
progress_bar.uptick(increment=0.2)
tlong = simple_moving_avg(fund, config[2])
if progress_bar is not None:
progress_bar.uptick(increment=0.2)
mshort = []
mmed = []
mlong = []
for i, close in enumerate(fund['Close']):
mshort.append(np.round((close - tshort[i]) / tshort[i] * 100.0, 3))
mmed.append(np.round((close - tmed[i]) / tmed[i] * 100.0, 3))
mlong.append(np.round((close - tlong[i]) / tlong[i] * 100.0, 3))
triple_exp_mov_average(fund,
config=[9, 21, 50],
plot_output=plot_output,
name=name,
view=view)
tshort_x, tshort2 = adjust_signals(fund, tshort, offset=config[0])
tmed_x, tmed2 = adjust_signals(fund, tmed, offset=config[1])
tlong_x, tlong2 = adjust_signals(fund, tlong, offset=config[2])
plot_short = {
"plot": tshort2,
"color": "blue",
"legend": f"{config[0]}-day MA",
"x": tshort_x
}
plot_med = {
"plot": tmed2,
"color": "orange",
"legend": f"{config[1]}-day MA",
"x": tmed_x
}
plot_long = {
"plot": tlong2,
"color": "black",
"legend": f"{config[2]}-day MA",
"x": tlong_x
}
if not out_suppress:
name3 = INDEXES.get(name, name)
name2 = name3 + \
' - Simple Moving Averages [{}, {}, {}]'.format(
config[0], config[1], config[2])
# legend = ['Price', f'{config[0]}-SMA',
# f'{config[1]}-SMA', f'{config[2]}-SMA']
# plots = [fund['Close'], tshort2, tmed2, tlong2]
# x_vals = [fund.index, tshort_x, tmed_x, tlong_x]
if plot_output:
candlestick_plot(fund,
title=name2,
additional_plts=[plot_short, plot_med, plot_long])
# generic_plotting(plots, x=x_vals,
# legend=legend, title=name2)
else:
filename = os.path.join(name, view,
f"simple_moving_averages_{name}.png")
candlestick_plot(fund,
title=name2,
filename=filename,
saveFig=True,
additional_plts=[plot_short, plot_med, plot_long])
# generic_plotting(plots, x=x_vals,
# legend=legend, title=name2, saveFig=True, filename=filename)
tma = dict()
tma['short'] = {'period': config[0]}
tma['medium'] = {'period': config[1]}
tma['long'] = {'period': config[2]}
tma['tabular'] = {'short': tshort, 'medium': tmed, 'long': tlong}
tma['metrics'] = {
f'{config[0]}-d': mshort,
f'{config[1]}-d': mmed,
f'{config[2]}-d': mlong
}
if progress_bar is not None:
progress_bar.uptick(increment=0.1)
tma['type'] = 'trend'
tma['length_of_data'] = len(tma['tabular']['short'])
tma['signals'] = find_crossovers(tma, fund)
return tma
def triple_exp_mov_average(fund: pd.DataFrame,
config=[9, 20, 50],
**kwargs) -> list:
"""Triple Exponential Moving Average
Arguments:
fund {pd.DataFrame} -- fund dataset
Optional Args:
plot_output {bool} -- (default: {True})
name {str} -- (default: {str})
view {str} -- file directory of plots (default: {''})
p_bar {ProgressBar} -- (default: {None})
Keyword Arguments:
config {list} -- look back period (default: {[9, 13, 50]})
Returns:
list -- plots and signals
"""
plot_output = kwargs.get('plot_output', True)
name = kwargs.get('name', '')
view = kwargs.get('view', '')
p_bar = kwargs.get('progress_bar')
out_suppress = kwargs.get('out_suppress', False)
tema = dict()
tshort = exponential_moving_avg(fund, config[0])
if p_bar is not None:
p_bar.uptick(increment=0.2)
tmed = exponential_moving_avg(fund, config[1])
if p_bar is not None:
p_bar.uptick(increment=0.2)
tlong = exponential_moving_avg(fund, config[2])
if p_bar is not None:
p_bar.uptick(increment=0.2)
tema['tabular'] = {'short': tshort, 'medium': tmed, 'long': tlong}
tema['short'] = {"period": config[0]}
tema['medium'] = {"period": config[1]}
tema['long'] = {"period": config[2]}
tshort_x, tshort2 = adjust_signals(fund, tshort, offset=config[0])
tmed_x, tmed2 = adjust_signals(fund, tmed, offset=config[1])
tlong_x, tlong2 = adjust_signals(fund, tlong, offset=config[2])
plot_short = {
"plot": tshort2,
"color": "blue",
"legend": f"{config[0]}-day MA",
"x": tshort_x
}
plot_med = {
"plot": tmed2,
"color": "orange",
"legend": f"{config[1]}-day MA",
"x": tmed_x
}
plot_long = {
"plot": tlong2,
"color": "black",
"legend": f"{config[2]}-day MA",
"x": tlong_x
}
mshort = []
mmed = []
mlong = []
for i, close in enumerate(fund['Close']):
mshort.append(np.round((close - tshort[i]) / tshort[i] * 100.0, 3))
mmed.append(np.round((close - tmed[i]) / tmed[i] * 100.0, 3))
mlong.append(np.round((close - tlong[i]) / tlong[i] * 100.0, 3))
if p_bar is not None:
p_bar.uptick(increment=0.2)
tema['metrics'] = {
f'{config[0]}-d': mshort,
f'{config[1]}-d': mmed,
f'{config[2]}-d': mlong
}
if not out_suppress:
name3 = INDEXES.get(name, name)
name2 = name3 + \
' - Exp Moving Averages [{}, {}, {}]'.format(
config[0], config[1], config[2])
# legend = ['Price', f'{config[0]}-EMA',
# f'{config[1]}-EMA', f'{config[2]}-EMA']
# plots = [fund['Close'], tshort2, tmed2, tlong2]
# x_vals = [fund.index, tshort_x, tmed_x, tlong_x]
if plot_output:
candlestick_plot(fund,
title=name2,
additional_plts=[plot_short, plot_med, plot_long])
# generic_plotting(plots, x=x_vals,
# legend=legend, title=name2)
else:
filename = os.path.join(name, view,
f"exp_moving_averages_{name}.png")
candlestick_plot(fund,
title=name2,
filename=filename,
saveFig=True,
additional_plts=[plot_short, plot_med, plot_long])
# generic_plotting(plots, x=x_vals,
# legend=legend, title=name2, saveFig=True, filename=filename)
if p_bar is not None:
p_bar.uptick(increment=0.2)
tema['type'] = 'trend'
tema['length_of_data'] = len(tema['tabular']['short'])
tema['signals'] = find_crossovers(tema, fund)
return tema
def generate_sar(fund: pd.DataFrame, **kwargs) -> dict:
"""Generate Stop and Reverse (SAR) Signal
Arguments:
fund {pd.DataFrame} -- fund dataset
Optional Args:
af {list} -- acceleration factor (default: {[0.02, 0.01]})
max_factor {float} -- max acceleration factor (default: {0.2})
period_offset {int} -- lookback period to establish a trend in starting
(default: {5})
plot_output {bool} -- (default: {True})
name {str} -- (default: {''})
view {str} -- (default: {''})
p_bar {ProgressBar} -- (default: {None})
Returns:
dict -- sar data object; signals (different AFs)
"""
# Note, higher/faster AF should be first of 2
ACC_FACTOR = kwargs.get('af', [0.02, 0.01])
MAX_FACTOR = kwargs.get('max_factor', 0.2)
period_offset = kwargs.get('period_offset', 5)
plot_output = kwargs.get('plot_output', True)
name = kwargs.get('name', '')
view = kwargs.get('view', '')
p_bar = kwargs.get('pbar')
sar_dict = dict()
signals = {"fast": [], "slow": []}
sig_names = ["fast", "slow"]
colors = ["blue", "black"]
indicators = []
signal = [0.0] * len(fund['Close'])
auto = autotrend(fund['Close'], periods=[4])
# Observe for an 'offset' amount of time before starting
ep_high = 0.0
ep_low = float('inf')
for i in range(period_offset):
signal[i] = fund['Close'][i]
if fund['Low'][i] < ep_low:
ep_low = fund['Low'][i]
if fund['High'][i] > ep_high:
ep_high = fund['High'][i]
# Determine an initial trend to start
trend = 'down'
e_p = ep_low
signal[period_offset - 1] = ep_high
if auto[period_offset - 1] > 0.0:
trend = 'up'
e_p = ep_high
signal[period_offset - 1] = ep_low
add_plts = []
if p_bar is not None:
p_bar.uptick(increment=0.1)
# Begin generating SAR signal, number of signals determined by number of AFs
for k, afx in enumerate(ACC_FACTOR):
a_f = afx
for i in range(period_offset, len(signal)):
data = None
date = fund.index[i].strftime("%Y-%m-%d")
if trend == 'down':
sar_i = signal[i - 1] - a_f * (signal[i - 1] - e_p)
if fund['High'][i] > sar_i:
# Broken downward trend. Stop and reverse!
a_f = afx
sar_i = e_p
e_p = fund['High'][i]
trend = 'up'
data = {
"type": 'bullish',
"value": f'downtrend broken-{sig_names[k]}',
"index": i,
"date": date
}
else:
if fund['Low'][i] < e_p:
e_p = fund['Low'][i]
a_f += afx
if a_f > MAX_FACTOR:
a_f = MAX_FACTOR
else:
sar_i = signal[i - 1] + a_f * (e_p - signal[i - 1])
if fund['Low'][i] < sar_i:
# Broken upward trend. Stop and reverse!
a_f = afx
sar_i = e_p
e_p = fund['Low'][i]
trend = 'down'
data = {
"type": 'bearish',
"value": f'upward broken-{sig_names[k]}',
"index": i,
"date": date
}
else:
if fund['High'][i] > e_p:
e_p = fund['High'][i]
a_f += afx
if a_f > MAX_FACTOR:
a_f = MAX_FACTOR
signal[i] = sar_i
if data is not None:
indicators.append(data)
signals[sig_names[k]] = signal.copy()
add_plts.append({
"plot": signals[sig_names[k]],
"style": 'scatter',
"legend": f'Parabolic SAR-{sig_names[k]}',
"color": colors[k]
})
if p_bar is not None:
p_bar.uptick(increment=0.2)
name2 = INDEXES.get(name, name)
title = f"{name2} - Parabolic SAR"
if plot_output:
candlestick_plot(fund, additional_plts=add_plts, title=title)
else:
filename = os.path.join(name, view, f"parabolic_sar_{name}")
candlestick_plot(fund,
additional_plts=add_plts,
title=title,
saveFig=True,
filename=filename)
if p_bar is not None:
p_bar.uptick(increment=0.1)
sar_dict['tabular'] = signals
sar_dict['signals'] = indicators
return sar_dict