def generate_bear_bull_signal(position: pd.DataFrame, **kwargs) -> dict:
"""Generate Bear Bull Signal
Arguments:
position {pd.DataFrame} -- dataset
Optional Args:
interval {int} -- size of exponential moving average window (default: {13})
plot_output {bool} -- (default: {True})
name {str} -- (default: {''})
p_bar {ProgressBar} -- (default: {None})
Returns:
dict -- [description]
"""
interval = kwargs.get('interval', 13)
plot_output = kwargs.get('plot_output', True)
name = kwargs.get('name', '')
p_bar = kwargs.get('p_bar')
bb_signal = {'bulls': [], 'bears': []}
ema = exponential_moving_avg(position, interval)
for i, high in enumerate(position['High']):
bb_signal['bulls'].append(high - ema[i])
if p_bar is not None:
p_bar.uptick(increment=0.1)
for i, low in enumerate(position['Low']):
bb_signal['bears'].append(low - ema[i])
if p_bar is not None:
p_bar.uptick(increment=0.1)
x = dates_extractor_list(position)
if plot_output:
name3 = INDEXES.get(name, name)
name2 = name3 + ' - Bull Power'
bar_chart(bb_signal['bulls'], position=position, title=name2, x=x)
name2 = name3 + ' - Bear Power'
bar_chart(bb_signal['bears'], position=position, title=name2, x=x)
return bb_signal
def bear_bull_feature_detection(bear_bull: dict, position: pd.DataFrame, **kwargs) -> dict:
"""Bear Bull Feature Detection
Arguments:
bear_bull {dict} -- bull bear data object
position {pd.DataFrame} -- dataset
Optional Args:
interval {int} -- size of exponential moving average window (default: {13})
bb_interval {list} -- list of sizes of bear and bull windows (default: {[4, 5, 6, 7, 8]})
plot_output {bool} -- (default: {True})
name {str} -- (default: {''})
p_bar {ProgressBar} -- (default {None})
view {str} -- (default: {''})
Returns:
dict -- [description]
"""
interval = kwargs.get('interval', 13)
bb_interval = kwargs.get('bb_interval', [4, 5, 6, 7, 8])
plot_output = kwargs.get('plot_output', True)
name = kwargs.get('name', '')
p_bar = kwargs.get('p_bar')
view = kwargs.get('view')
# Determine the slope of the ema at given points
ema = exponential_moving_avg(position, interval)
ema_slopes = [0.0] * (interval-1)
for i in range(interval-1, len(ema)):
x = list(range(i-(interval-1), i))
y = ema[i-(interval-1): i]
reg = linregress(x, y=y)
ema_slopes.append(reg[0])
if p_bar is not None:
p_bar.uptick(increment=0.1)
incr = 0.6 / float(len(bb_interval))
state = [0.0] * len(ema)
features = []
for bb in bb_interval:
# Determine the slope of the bear power signal at given points
bear_slopes = [0.0] * (bb-1)
for i in range(bb-1, len(ema)):
x = list(range(i-(bb-1), i))
y = bear_bull['tabular']['bears'][i-(bb-1): i]
reg = linregress(x, y=y)
bear_slopes.append(reg[0])
# Determine the slope of the bull power signal at given points
bull_slopes = [0.0] * (bb-1)
for i in range(bb-1, len(ema)):
x = list(range(i-(bb-1), i))
y = bear_bull['tabular']['bulls'][i-(bb-1): i]
reg = linregress(x, y=y)
bull_slopes.append(reg[0])
for i in range(1, len(ema)):
data = None
date = position.index[i].strftime("%Y-%m-%d")
if ema_slopes[i] > 0.0:
if bear_bull['tabular']['bears'][i] < 0.0:
if bear_slopes[i] > 0.0:
# Determine the basic (first 2 conditions) bullish state (+1)
state[i] += 1.0
if bear_bull['tabular']['bulls'][i] > bear_bull['tabular']['bulls'][i-1]:
state[i] += 0.5
if position['Close'][i] < position['Close'][i-1]:
# Need to find bullish divergence!
state[i] += 1.0
data = {
"type": 'bullish',
"value": f'bullish divergence: {bb}d interval',
"index": i,
"date": date
}
if ema_slopes[i] < 0.0:
if bear_bull['tabular']['bulls'][i] > 0.0:
if bull_slopes[i] < 0.0:
# Determine the basic (first 2 conditions) bearish state (+1)
state[i] += -1.0
if bear_bull['tabular']['bears'][i] < bear_bull['tabular']['bears'][i-1]:
state[i] += -0.5
if position['Close'][i] > position['Close'][i-1]:
# Need to find bearish divergence!
state[i] += -1.0
data = {
"type": 'bearish',
"value": f'bearish divergence: {bb}d interval',
"index": i,
"date": date
}
if data is not None:
features.append(data)
if p_bar is not None:
p_bar.uptick(increment=incr)
bear_bull['signals'] = filter_features(features, plot_output=plot_output)
bear_bull['length_of_data'] = len(bear_bull['tabular']['bears'])
weights = [1.0, 0.75, 0.45, 0.1]
state2 = [0.0] * len(state)
for ind, s in enumerate(state):
if s != 0.0:
state2[ind] += s
# Smooth the curves
if ind - 1 >= 0:
state2[ind-1] += s * weights[1]
if ind + 1 < len(state):
state2[ind+1] += s * weights[1]
if ind - 2 >= 0:
state2[ind-2] += s * weights[2]
if ind + 2 < len(state):
state2[ind+2] += s * weights[2]
if ind - 3 >= 0:
state2[ind-3] += s * weights[3]
if ind + 3 < len(state):
state2[ind+3] += s * weights[3]
state3 = exponential_moving_avg(state2, 7, data_type='list')
norm = normalize_signals([state3])
state4 = norm[0]
title = 'Bear Bull Power Metrics'
if plot_output:
dual_plotting(position['Close'], state4, 'Price',
'Bear Bull Power', title=title)
else:
filename = os.path.join(name, view, f"bear_bull_power_{name}.png")
dual_plotting(position['Close'], state4, 'Price', 'Metrics', title=title,
saveFig=True, filename=filename)
bear_bull['metrics'] = state4
if p_bar is not None:
p_bar.uptick(increment=0.1)
return bear_bull
def generate_total_power_signal(position: pd.DataFrame, **kwargs) -> dict:
"""Generate Total Power Signal
Arguments:
position {pd.DataFrame} -- fund dataset
Optional Args:
lookback {int} -- period for total power signal (default: {45})
powerback {int} -- period for ema (default: {10})
plot_output {bool} -- (default: {True})
name {str} -- (default: {''})
p_bar {ProgressBar} -- (default: {None})
view {str} -- (default: {''})
Returns:
dict -- cumulative signals bear, bull, total
"""
lookback = kwargs.get('lookback', 45)
powerback = kwargs.get('powerback', 10)
plot_output = kwargs.get('plot_output', True)
name = kwargs.get('name', '')
p_bar = kwargs.get('p_bar')
view = kwargs.get('view')
signals = {
"bears_raw": [],
"bulls_raw": [],
"total": [],
"bears": [],
"bulls": []
}
ema = exponential_moving_avg(position, powerback)
if p_bar is not None:
p_bar.uptick(increment=0.1)
for i, high in enumerate(position['High']):
if high - ema[i] > 0.0:
signals['bulls_raw'].append(1.0)
else:
signals['bulls_raw'].append(0.0)
for i, low in enumerate(position['Low']):
if low - ema[i] < 0.0:
signals['bears_raw'].append(1.0)
else:
signals['bears_raw'].append(0.0)
if p_bar is not None:
p_bar.uptick(increment=0.1)
# Create look back period signals
for i in range(lookback-1):
signals['bulls'].append(sum(signals['bulls_raw'][0:i+1]))
signals['bears'].append(sum(signals['bears_raw'][0:i+1]))
if p_bar is not None:
p_bar.uptick(increment=0.1)
for i in range(lookback-1, len(ema)):
summer = sum(signals['bulls_raw'][i-(lookback-1): i])
signals['bulls'].append(summer)
summer = sum(signals['bears_raw'][i-(lookback-1): i])
signals['bears'].append(summer)
if p_bar is not None:
p_bar.uptick(increment=0.1)
# Compute the total power signal
for i, bull in enumerate(signals['bulls']):
total = np.abs(bull - signals['bears'][i])
signals['total'].append(total)
if p_bar is not None:
p_bar.uptick(increment=0.1)
# Adjust the signals, pop the 'raw' lists
signals.pop('bulls_raw')
signals.pop('bears_raw')
for i, tot in enumerate(signals['total']):
signals['total'][i] = 100.0 * tot / float(lookback)
if p_bar is not None:
p_bar.uptick(increment=0.1)
for i, bull in enumerate(signals['bulls']):
signals['bulls'][i] = 100.0 * bull / float(lookback)
for i, bear in enumerate(signals['bears']):
signals['bears'][i] = 100.0 * bear / float(lookback)
if p_bar is not None:
p_bar.uptick(increment=0.1)
name2 = INDEXES.get(name, name)
title = name2 + ' - Total Power'
if plot_output:
dual_plotting(position['Close'],
[signals['bears'], signals['bulls'], signals['total']],
'Price',
['Bear', 'Bull', 'Total'],
title=title)
else:
filename = os.path.join(name, view, f"total_power_{name}.png")
dual_plotting(position['Close'],
[signals['bears'], signals['bulls'], signals['total']],
'Price',
['Bear', 'Bull', 'Total'],
title=title,
saveFig=True,
filename=filename)
if p_bar is not None:
p_bar.uptick(increment=0.1)
return signals
def total_power_feature_detection(tp: dict, position: pd.DataFrame, **kwargs) -> dict:
"""Total Power Feature Detection / Metrics
Arguments:
tp {dict} -- total power data object
position {pd.DataFrame} -- dataset
Optional Args:
adj_level {float} -- threshold level of indicators (default: {72.0})
plot_output {bool} -- (default: {True})
name {str} -- (default: {''})
p_bar {ProgressBar} -- (default: {None})
view {str} -- (default: {''})
Returns:
dict -- tp
"""
adj_level = kwargs.get('adj_level', 72.0)
plot_output = kwargs.get('plot_output', True)
name = kwargs.get('name', '')
p_bar = kwargs.get('p_bar')
view = kwargs.get('view')
tab = tp['tabular']
metrics = [0.0] * len(tab['total'])
features = []
if tab['bulls'][0] > tab['bears'][0]:
on_top = 'bull'
else:
on_top = 'bear'
tot_above = ''
for i, tot in enumerate(tab['total']):
date = position.index[i].strftime("%Y-%m-%d")
if (tot >= 99.0) and (tab['bulls'][i] >= 99.0):
metrics[i] += 3.0
data = {
"type": 'bullish',
"value": 'full bullish signal: 100%',
"index": i,
"date": date
}
features.append(data)
if (tot >= 99.0) and (tab['bears'][i] >= 99.0):
metrics[i] += -3.0
data = {
"type": 'bearish',
"value": 'full bearish signal: 100%',
"index": i,
"date": date
}
features.append(data)
if (on_top == 'bull') and (tab['bears'][i] > tab['bulls'][i]):
metrics[i] += -1.0
on_top = 'bear'
data = {
"type": 'bearish',
"value": 'bear-bull crossover',
"index": i,
"date": date
}
features.append(data)
if (on_top == 'bear') and (tab['bulls'][i] > tab['bears'][i]):
metrics[i] += -1.0
on_top = 'bull'
data = {
"type": 'bullish',
"value": 'bear-bull crossover',
"index": i,
"date": date
}
features.append(data)
if tot > tab['bulls'][i]:
tot_above = 'bull'
if tot > tab['bears'][i]:
tot_above = 'bear'
if (tot_above == 'bull') and (tab['bulls'][i] > tot):
tot_above = ''
metrics[i] += 1.0
data = {
"type": 'bullish',
"value": 'bull-total crossover',
"index": i,
"date": date
}
features.append(data)
if (tot_above == 'bear') and (tab['bears'][i] > tot):
tot_above = ''
metrics[i] += -1.0
data = {
"type": 'bearish',
"value": 'bear-total crossover',
"index": i,
"date": date
}
features.append(data)
if i > 0:
if (tab['bulls'][i-1] < adj_level) and (tab['bulls'][i] > adj_level):
metrics[i] += 1.0
data = {
"type": 'bullish',
"value": 'bull-adjustment level crossover',
"index": i,
"date": date
}
features.append(data)
if (tab['bears'][i-1] < adj_level) and (tab['bears'][i] > adj_level):
metrics[i] += -1.0
data = {
"type": 'bearish',
"value": 'bear-adjustment level crossover',
"index": i,
"date": date
}
features.append(data)
if p_bar is not None:
p_bar.uptick(increment=0.1)
weights = [1.0, 0.85, 0.6, 0.25]
state2 = [0.0] * len(metrics)
for ind, s in enumerate(metrics):
if s != 0.0:
state2[ind] += s
# Smooth the curves
if ind - 1 >= 0:
state2[ind-1] += s * weights[1]
if ind + 1 < len(metrics):
state2[ind+1] += s * weights[1]
if ind - 2 >= 0:
state2[ind-2] += s * weights[2]
if ind + 2 < len(metrics):
state2[ind+2] += s * weights[2]
if ind - 3 >= 0:
state2[ind-3] += s * weights[3]
if ind + 3 < len(metrics):
state2[ind+3] += s * weights[3]
metrics = exponential_moving_avg(state2, 7, data_type='list')
norm = normalize_signals([metrics])
metrics = norm[0]
name2 = INDEXES.get(name, name)
title = name2 + ' - Total Power Metrics'
if plot_output:
dual_plotting(position['Close'], metrics, 'Price', 'Metrics')
for feat in features:
print(f"Total Power: {feat}")
else:
filename = os.path.join(name, view, f"total_pwr_metrics_{name}.png")
dual_plotting(position['Close'], metrics, 'Price',
'Metrics', title=title, saveFig=True, filename=filename)
tp['metrics'] = metrics
tp['signals'] = features
tp['length_of_data'] = len(tp['tabular']['bears'])
if p_bar is not None:
p_bar.uptick(increment=0.1)
return tp