def cluster_dates(cluster_list: list, fund: pd.DataFrame) -> list:
""" Adds non-zero cluster values with date, price, and index """
dates = []
for i in range(len(cluster_list)):
if cluster_list[i] != 0:
dates.append([date_extractor(fund.index[i], _format='str'),
fund['Close'][i], cluster_list[i], i])
return dates
def add_daterange(original: pd.DataFrame, extrema: dict,
num_feature_points: int) -> dict:
""" Add Date Range
Looks at index ranges of 'extrema' and adds actual dates from 'original' to 'extrema'
Arguments:
original {pd.DataFrame} -- fund dataset
extrema {dict} -- extrema data object
num_feature_points {int} -- selectable number of points to add dates
Returns:
dict -- extrema data object
"""
for feat in extrema['features']:
if feat:
first_ind = feat['indexes'][0][0]
last_ind = feat['indexes'][num_feature_points - 1][0]
start = date_extractor(original.index[first_ind], _format='str')
end = date_extractor(original.index[last_ind], _format='str')
feat['daterange'] = start + ' : ' + end
return extrema
def period_strength(fund_name: str, tickers: dict, periods: list,
**kwargs) -> list:
"""Period Strength
Try to provide ratio evaluations of 'fund' vs. market and sector. Update utilizes sectors.json
file to pull information.
Arguments:
fund_name {str} -- 'MSFT', for example
tickers {dict} -- entire downloaded data object
periods {list} - list of lookback periods
Optional Args:
sector {str} -- name of sector, 'VGT' for Tech, for example (default: {''})
sector_data {pd.DataFrame} -- data of sector (default: {None})
Returns:
list -- list of ratio data objects
"""
sector = kwargs.get('sector', '')
sector_data = kwargs.get('sector_data')
ratio = []
hasSP = False
hasSector = False
sp = get_SP500_df(tickers)
if sp is not None:
hasSP = True
if sector != '':
if sector_data is not None:
sec = sector_data
hasSector = True
fund = tickers[fund_name]
for period in periods:
entry = {}
entry['period'] = period
entry['dates'] = date_extractor(fund.index[len(fund.index)-period], _format='str') + \
" : " + \
date_extractor(fund.index[len(fund.index)-1], _format='str')
if hasSP:
entry['sp500'] = {}
sp_temp = list(sp['Adj Close'])
sp_temp = sp_temp[len(sp_temp) - period:len(sp_temp) + 1]
f_temp = list(fund['Adj Close'])
f_temp = f_temp[len(f_temp) - period:len(f_temp) + 1]
rat = normalized_ratio(f_temp, sp_temp, data_type='list')
entry['sp500']['avg'] = np.round(np.mean(rat), 6)
entry['sp500']['stdev'] = np.round(np.std(rat), 6)
entry['sp500']['min'] = np.min(rat)
entry['sp500']['max'] = np.max(rat)
entry['sp500']['current'] = rat[-1]
if hasSector:
entry['sector'] = {}
entry['sector']['name'] = sector
sp_temp = list(sec['Adj Close'])
sp_temp = sp_temp[len(sp_temp) - period:len(sp_temp) + 1]
f_temp = list(fund['Adj Close'])
f_temp = f_temp[len(f_temp) - period:len(f_temp) + 1]
rat = normalized_ratio(f_temp, sp_temp, data_type='list')
entry['sector']['avg'] = np.round(np.mean(rat), 6)
entry['sector']['stdev'] = np.round(np.std(rat), 6)
entry['sector']['min'] = np.min(rat)
entry['sector']['max'] = np.max(rat)
entry['sector']['current'] = rat[-1]
ratio.append(entry)
return ratio
def run_dev(script: list):
"""Run Development Script
Script that is for implementing new content
Arguments:
script {list} -- dataset, funds, periods, config
Returns:
dict -- analysis object of fund data
"""
dataset = script[0]
funds = script[1]
periods = script[2]
config = script[3]
# Start of automated process
analysis = {}
clock = start_clock()
for fund_name in funds:
if fund_name in SKIP_INDEXES:
continue
fund_print = INDEXES.get(fund_name, fund_name)
print("")
print(f"~~{fund_print}~~")
create_sub_temp_dir(fund_name, sub_periods=config['period'])
analysis[fund_name] = {}
analysis[fund_name]['metadata'] = get_api_metadata(
fund_name,
max_close=max(dataset[periods[0]][fund_name]['Close']),
data=dataset[periods[0]][fund_name])
###################### START OF PERIOD LOOPING #############################
for i, period in enumerate(periods):
fund_data = {}
fund = dataset[period][fund_name]
start = date_extractor(fund.index[0], _format='str')
end = date_extractor(fund.index[-1], _format='str')
fund_data['dates_covered'] = {'start': str(start), 'end': str(end)}
fund_data['name'] = fund_name
fund_print2 = fund_print + f" ({period}) "
p = ProgressBar(config['process_steps'],
name=fund_print2,
offset=clock)
p.start()
fund_data['statistics'] = get_high_level_stats(fund)
fund_data['clustered_osc'] = cluster_oscs(fund,
function='all',
filter_thresh=3,
name=fund_name,
plot_output=False,
progress_bar=p,
view=period)
fund_data['full_stochastic'] = full_stochastic(fund,
name=fund_name,
plot_output=False,
out_suppress=False,
progress_bar=p,
view=period)
fund_data['rsi'] = RSI(fund,
name=fund_name,
plot_output=False,
out_suppress=False,
progress_bar=p,
view=period)
fund_data['ultimate'] = ultimate_oscillator(fund,
name=fund_name,
plot_output=False,
out_suppress=False,
progress_bar=p,
view=period)
fund_data['awesome'] = awesome_oscillator(fund,
name=fund_name,
plot_output=False,
progress_bar=p,
view=period)
fund_data['momentum_oscillator'] = momentum_oscillator(
fund,
name=fund_name,
plot_output=False,
progress_bar=p,
view=period)
fund_data['on_balance_volume'] = on_balance_volume(
fund,
plot_output=False,
name=fund_name,
progress_bar=p,
view=period)
fund_data['simple_moving_average'] = triple_moving_average(
fund,
plot_output=False,
name=fund_name,
progress_bar=p,
view=period)
fund_data['exp_moving_average'] = triple_exp_mov_average(
fund,
plot_output=False,
name=fund_name,
progress_bar=p,
view=period)
fund_data['sma_swing_trade'] = moving_average_swing_trade(
fund,
plot_output=False,
name=fund_name,
progress_bar=p,
view=period)
fund_data['ema_swing_trade'] = moving_average_swing_trade(
fund,
function='ema',
plot_output=False,
name=fund_name,
progress_bar=p,
view=period)
fund_data['hull_moving_average'] = hull_moving_average(
fund,
plot_output=False,
name=fund_name,
progress_bar=p,
view=period)
fund_data['macd'] = mov_avg_convergence_divergence(
fund,
plot_output=False,
name=fund_name,
progress_bar=p,
view=period)
fund_data['bear_bull_power'] = bear_bull_power(fund,
plot_output=False,
name=fund_name,
progress_bar=p,
view=period)
fund_data['total_power'] = total_power(fund,
plot_output=False,
name=fund_name,
progress_bar=p,
view=period)
fund_data['bollinger_bands'] = bollinger_bands(fund,
plot_output=False,
name=fund_name,
progress_bar=p,
view=period)
fund_data['commodity_channels'] = commodity_channel_index(
fund,
plot_output=False,
name=fund_name,
progress_bar=p,
view=period)
fund_data['rate_of_change'] = rate_of_change_oscillator(
fund,
plot_output=False,
name=fund_name,
progress_bar=p,
view=period)
fund_data['know_sure_thing'] = know_sure_thing(fund,
plot_output=False,
name=fund_name,
progress_bar=p,
view=period)
fund_data['average_true_range'] = average_true_range(
fund,
plot_output=False,
name=fund_name,
progress_bar=p,
view=period)
fund_data['adx'] = average_directional_index(
fund,
atr=fund_data['average_true_range']['tabular'],
plot_output=False,
name=fund_name,
progress_bar=p,
view=period)
fund_data['parabolic_sar'] = parabolic_sar(
fund,
adx_tabular=fund_data['adx']['tabular'],
plot_output=False,
name=fund_name,
progress_bar=p,
view=period)
fund_data['demand_index'] = demand_index(fund,
plot_output=False,
name=fund_name,
progress_bar=p,
view=period)
if 'no_index' not in config['state']:
strength, match_data = relative_strength(
fund_name,
full_data_dict=dataset[period],
config=config,
plot_output=False,
meta=analysis[fund_name]['metadata'],
progress_bar=p,
period=period,
interval=config['interval'][i],
view=period)
fund_data['relative_strength'] = strength
fund_data['statistics']['risk_ratios'] = risk_comparison(
fund,
dataset[period]['^GSPC'],
dataset[period]['^IRX'],
sector_data=match_data)
p.uptick()
# Support and Resistance Analysis
fund_data['support_resistance'] = find_resistance_support_lines(
fund,
name=fund_name,
plot_output=False,
progress_bar=p,
view=period)
# Feature Detection Block
fund_data['features'] = {}
fund_data['features'][
'head_shoulders'] = feature_detection_head_and_shoulders(
fund,
name=fund_name,
plot_output=False,
progress_bar=p,
view=period)
fund_data['candlesticks'] = candlesticks(fund,
name=fund_name,
plot_output=False,
view=period,
progress_bar=p)
fund_data['price_gaps'] = analyze_price_gaps(fund,
name=fund_name,
plot_output=False,
progress_bar=p,
view=period)
# Get Trendlines
fund_data['trendlines'] = get_trendlines(
fund,
name=fund_name,
plot_output=False,
progress_bar=p,
view=period,
meta=analysis[fund_name]['metadata'])
# Various Fund-specific Metrics
fund_data['futures'] = future_returns(fund, progress_bar=p)
# Parse through indicators and pull out latest signals (must be last)
fund_data['last_signals'] = assemble_last_signals(
fund_data,
fund=fund,
name=fund_name,
view=period,
progress_bar=p,
plot_output=False)
p.end()
analysis[fund_name][period] = fund_data
analysis[fund_name]['synopsis'] = generate_synopsis(analysis,
name=fund_name)
return analysis, clock
def rsi_divergence(position: pd.DataFrame, rsi_data: dict, **kwargs) -> dict:
"""RSI Divergence:
1. Cross outside threshold at Price A
2. Cross inside threshold
3. Cross outside threshold at Price B
4. Exit [inside] threshold, where Price B is more extreme than A
Arguments:
position {pd.DataFrame} -- dataset
rsi_data {dict} -- rsi data object
Optional Args:
plot_output {bool} -- (default: {True})
p_bar {ProgressBar} -- (default: {None})
Returns:
dict -- rsi data object
"""
plot_output = kwargs.get('plot_output', True)
p_bar = kwargs.get('p_bar')
signal = rsi_data['tabular']
ovb_th = rsi_data['thresholds']['overbought']
ovs_th = rsi_data['thresholds']['oversold']
divs = [0.0] * len(signal)
state = 'n'
maxima = 0.0
minima = 0.0
prices = [0.0, 0.0]
rsi_vals = [0.0, 0.0]
for i, sig in enumerate(signal):
# Start with bullish divergence
if (state == 'n') and (sig <= ovs_th[i]):
state = 'u1'
rsi_vals[0] = sig
elif state == 'u1':
if sig <= rsi_vals[0]:
rsi_vals[0] = sig
prices[0] = position['Close'][i]
else:
state = 'u2'
maxima = sig
elif state == 'u2':
if sig >= maxima:
if sig >= ovb_th[i]:
state = 'e1'
rsi_vals[0] = sig
else:
maxima = sig
else:
state = 'u3'
rsi_vals[1] = sig
elif state == 'u3':
if sig <= rsi_vals[1]:
prices[1] = position['Close'][i]
rsi_vals[1] = sig
else:
if rsi_vals[1] <= ovs_th[i]:
if (prices[1] < prices[0]) and (rsi_vals[0] < rsi_vals[1]):
# Bullish divergence!
divs[i] = 1.0
rsi_data['bullish'].append([
date_extractor(position.index[i], _format='str'),
position['Close'][i], i, "divergence"
])
state = 'n'
else:
state = 'n'
else:
state = 'n'
# Start with bearish divergence
elif (state == 'n') and (sig >= ovb_th[i]):
state = 'e1'
rsi_vals[0] = sig
elif state == 'e1':
if sig >= rsi_vals[0]:
rsi_vals[0] = sig
prices[0] = position['Close'][i]
else:
state = 'e2'
minima = sig
elif state == 'e2':
if sig <= minima:
if sig <= ovs_th[i]:
state = 'u1'
rsi_vals[0] = sig
else:
minima = sig
else:
state = 'e3'
rsi_vals[1] = sig
elif state == 'e3':
if sig >= rsi_vals[1]:
prices[1] = position['Close'][i]
rsi_vals[1] = sig
else:
if rsi_vals[1] >= ovb_th[i]:
if (prices[1] > prices[0]) and (rsi_vals[0] > rsi_vals[1]):
# Bearish divergence!
divs[i] = -1.0
rsi_data['bearish'].append([
date_extractor(position.index[i], _format='str'),
position['Close'][i], i, "divergence"
])
state = 'n'
else:
state = 'n'
else:
state = 'n'
rsi_data['divergence'] = divs
if plot_output:
dual_plotting(position['Close'], divs, 'Price', 'RSI', title='Divs')
if p_bar is not None:
p_bar.uptick(increment=0.1)
return rsi_data
def determine_rsi_swing_rejection(position: pd.DataFrame, rsi_data: dict,
**kwargs) -> dict:
""" Find bullish / bearish and RSI indicators
1. go beyond threshold
2. go back within thresholds
3. have local minima/maxima inside thresholds
4. exceed max/min (bull/bear) of previous maxima/minima
Arguments:
position {pd.DataFrame} -- fund data
rsi_signal {list} -- pure RSI signal
rsi_data {dict} -- RSI data object
Optional Args:
p_bar {ProgressBar} -- (default: {None})
Returns:
dict -- rsi data object
"""
p_bar = kwargs.get('p_bar', None)
thresholds = rsi_data['thresholds']
rsi_signal = rsi_data['tabular']
if thresholds is None:
thresholds = dict()
thresholds['oversold'] = [30.0] * len(position['Close'])
thresholds['overbought'] = [70.0] * len(position['Close'])
LOW_TH = thresholds['oversold']
HIGH_TH = thresholds['overbought']
rsi_data['bullish'] = []
rsi_data['bearish'] = []
indicator = []
increment = 0.2 / float(len(rsi_signal))
state = 0
minima = 0.0
maxima = 0.0
for i in range(len(rsi_signal)):
if (state == 0) and (rsi_signal[i] <= LOW_TH[i]):
# Start of a bullish signal
state = 1
indicator.append(0.0)
elif (state == 1) and (rsi_signal[i] > LOW_TH[i]):
state = 2
maxima = rsi_signal[i]
indicator.append(0.0)
elif (state == 2):
if rsi_signal[i] >= maxima:
if rsi_signal[i] >= HIGH_TH[i]:
state = 5
else:
maxima = rsi_signal[i]
else:
minima = rsi_signal[i]
state = 3
indicator.append(0.0)
elif (state == 3):
if rsi_signal[i] <= LOW_TH[i]:
# Failed attempted breakout
state = 1
elif rsi_signal[i] <= minima:
minima = rsi_signal[i]
else:
state = 4
indicator.append(0.0)
elif (state == 4):
if rsi_signal[i] > maxima:
# Have found a bullish breakout!
rsi_data['bullish'].append([
date_extractor(position.index[i], _format='str'),
position['Close'][i], i, "swing rejection"
])
state = 0
minima = 0.0
maxima = 0.0
indicator.append(1.0)
else:
if rsi_signal[i] <= LOW_TH[i]:
state = 1
indicator.append(0.0)
elif (state == 0) and (rsi_signal[i] >= HIGH_TH[i]):
state = 5
indicator.append(0.0)
elif (state == 5) and (rsi_signal[i] < HIGH_TH[i]):
state = 6
minima = rsi_signal[i]
indicator.append(0.0)
elif (state == 6):
if rsi_signal[i] <= minima:
if rsi_signal[i] <= LOW_TH[i]:
state = 1
else:
minima = rsi_signal[i]
else:
maxima = rsi_signal[i]
state = 7
indicator.append(0.0)
elif (state == 7):
if rsi_signal[i] >= HIGH_TH[i]:
# Failed attempted breakout
state = 5
elif rsi_signal[i] >= maxima:
maxima = rsi_signal[i]
else:
state = 8
indicator.append(0.0)
elif (state == 8):
if rsi_signal[i] < minima:
rsi_data['bearish'].append([
date_extractor(position.index[i], _format='str'),
position['Close'][i], i, "swing rejection"
])
state = 0
minima = 0.0
maxima = 0.0
indicator.append(-1.0)
else:
if rsi_signal[i] >= HIGH_TH[i]:
state = 5
indicator.append(0.0)
else:
indicator.append(0.0)
if p_bar is not None:
p_bar.uptick(increment=increment)
rsi_data['indicator'] = indicator
return rsi_data
def get_stoch_divergences(position: pd.DataFrame, full_stoch: dict,
**kwargs) -> dict:
"""Get Stoch Divergences
Arguments:
position {pd.DataFrame} -- dataset
full_stoch {dict} -- stoch data object
Optional Args:
name {str} -- (default: {''})
plot_output {bool} -- (default: {True})
out_suppress {bool} -- (default: {True})
p_bar {ProgressBar} -- (default: {None})
Returns:
dict -- stoch data object
"""
name = kwargs.get('name', '')
plot_output = kwargs.get('plot_output', True)
out_suppress = kwargs.get('out_suppress', True)
p_bar = kwargs.get('p_bar')
view = kwargs.get('view')
OVER_BOUGHT = 80.0
OVER_SOLD = 20.0
fast_k = full_stoch['tabular']['fast_k']
state = 'n'
# Look for divergences + fast_k at 50
prices = [0.0, 0.0]
s_vals = [0.0, 0.0]
for i, fast in enumerate(fast_k):
# Bearish divergences
if (state == 'n') and fast >= OVER_BOUGHT:
state = 'b1'
s_vals[0] = fast
elif state == 'b1':
if fast < s_vals[0]:
prices[0] = position['Close'][i]
state = 'b2'
else:
s_vals[0] = fast
elif state == 'b2':
if fast < OVER_BOUGHT:
state = 'b3'
s_vals[1] = fast
elif fast > s_vals[0]:
s_vals[0] = fast
state = 'b1'
elif state == 'b3':
if fast > s_vals[1]:
state = 'b4'
s_vals[1] = fast
else:
s_vals[1] = fast
elif state == 'b4':
if fast < s_vals[1]:
prices[1] = position['Close'][i - 1]
if (prices[0] < prices[1]) and (s_vals[0] > s_vals[1]):
full_stoch['bearish'].append([
date_extractor(position.index[i], _format='str'),
position['Close'][i - 1], i, "divergence"
])
full_stoch['indicator'][i] += -1.5
state = 'n'
else:
state = 'n'
else:
s_vals[1] = fast
if s_vals[1] > s_vals[0]:
s_vals[0] = fast
state = 'b1'
# Bullish divergences
elif (state == 'n') and fast <= OVER_SOLD:
state = 's1'
s_vals[0] = fast
elif state == 's1':
if fast > s_vals[0]:
prices[0] = position['Close'][i]
state = 's2'
else:
s_vals[0] = fast
elif state == 's2':
if fast > OVER_SOLD:
state = 's3'
s_vals[1] = fast
elif fast < s_vals[0]:
s_vals[0] = fast
state = 's1'
elif state == 's3':
if fast < s_vals[1]:
state = 's4'
s_vals[1] = fast
else:
s_vals[1] = fast
elif state == 's4':
if fast > s_vals[1]:
prices[1] = position['Close'][i - 1]
if (prices[0] > prices[1]) and (s_vals[0] < s_vals[1]):
full_stoch['bullish'].append([
date_extractor(position.index[i], _format='str'),
position['Close'][i - 1], i, "divergence"
])
full_stoch['indicator'][i] += 1.5
state = 'n'
else:
state = 'n'
else:
s_vals[1] = fast
if s_vals[1] < s_vals[0]:
s_vals[0] = fast
state = 's1'
if p_bar is not None:
p_bar.uptick(increment=0.2)
if not out_suppress:
name3 = INDEXES.get(name, name)
name2 = name3 + ' - Stochastic'
if plot_output:
dual_plotting(position['Close'],
full_stoch['indicator'],
'Position Price',
'Oscillator Signal',
title=name2)
else:
filename = os.path.join(name, view, f"stochastic_{name}.png")
dual_plotting(position['Close'],
full_stoch['indicator'],
'Position Price',
'Stochastic Oscillator',
x_label='Trading Days',
title=name2,
saveFig=True,
filename=filename)
return full_stoch
def get_crossover_features(position: pd.DataFrame, full_stoch: dict,
**kwargs) -> dict:
"""Get Crossover Features
Look for a %K cross over %d (fast) then cross inside the 80/20 bars.
Look for a slow %K cross over slow %d then cross inside the 80/20 bars.
Arguments:
position {pd.DataFrame} -- dataset
full_stoch {dict} -- full stochastic object
Optional Args:
p_bar {ProgressBar} -- (default: {None})
Returns:
dict -- full stochastic object
"""
p_bar = kwargs.get('p_bar')
OVER_BOUGHT = 80.0
OVER_SOLD = 20.0
fast_k = full_stoch['tabular']['fast_k']
smooth_k = full_stoch['tabular']['smooth_k']
d_sma = full_stoch['tabular']['slow_d']
bearish = []
bullish = []
stochastic = [0.0] * (len(fast_k))
state = 'n'
# Look at crossovers and over-X positions of fast_k & smooth_k
for i, fast in enumerate(fast_k):
# Bearish / overbought signaling
if (state == 'n') and (fast >= OVER_BOUGHT):
state = 'b1'
elif (state == 'b1'):
if (fast >= OVER_BOUGHT):
if (smooth_k[i] >= OVER_BOUGHT):
state = 'b2'
else:
state = 'n'
elif (state == 'b2') and (fast < smooth_k[i]):
state = 'b3'
elif (state == 'b3'):
if (fast < OVER_BOUGHT):
bearish.append([
date_extractor(position.index[i], _format='str'),
position['Close'][i], i, "crossover: fast-k/smooth-k"
])
stochastic[i] += -1.0
state = 'n'
# Bullish / oversold signaling
elif (state == 'n') and (fast <= OVER_SOLD):
state = 's1'
elif (state == 's1'):
if (fast <= OVER_SOLD):
if (smooth_k[i] <= OVER_SOLD):
state = 's2'
else:
state = 'n'
elif (state == 's2') and (fast > smooth_k[i]):
state = 's3'
elif (state == 's3'):
if (fast > OVER_SOLD):
bullish.append([
date_extractor(position.index[i], _format='str'),
position['Close'][i], i, "crossover: fast-k/smooth-k"
])
stochastic[i] += 1.0
state = 'n'
if p_bar is not None:
p_bar.uptick(increment=0.1)
# Look at crossovers and over-X positions of smooth_k & slow_d
state = 'n'
for i, slow in enumerate(smooth_k):
# Bearish / overbought signaling
if (state == 'n') and (slow >= OVER_BOUGHT):
state = 'b1'
elif (state == 'b1'):
if (slow >= OVER_BOUGHT):
if (d_sma[i] >= OVER_BOUGHT):
state = 'b2'
else:
state = 'n'
elif (state == 'b2') and (slow < d_sma[i]):
state = 'b3'
elif (state == 'b3'):
if (slow < OVER_BOUGHT):
bearish.append([
date_extractor(position.index[i], _format='str'),
position['Close'][i], i, "crossover: smooth-k/slow-d"
])
stochastic[i] += -1.0
state = 'n'
# Bullish / oversold signaling
elif (state == 'n') and (slow <= OVER_SOLD):
state = 's1'
elif (state == 's1'):
if (slow <= OVER_SOLD):
if (d_sma[i] <= OVER_SOLD):
state = 's2'
else:
state = 'n'
elif (state == 's2') and (slow > d_sma[i]):
state = 's3'
elif (state == 's3'):
if (slow > OVER_SOLD):
bullish.append([
date_extractor(position.index[i], _format='str'),
position['Close'][i], i, "crossover: smooth-k/slow-d"
])
stochastic[i] += 1.0
state = 'n'
if p_bar is not None:
p_bar.uptick(increment=0.1)
full_stoch['indicator'] = stochastic
full_stoch['bullish'] = bullish
full_stoch['bearish'] = bearish
return full_stoch
def find_ult_osc_features(position: pd.DataFrame, ultimate: dict,
**kwargs) -> list:
"""Find Ultimate Oscilator Features
Arguments:
position {pd.DataFrame} -- dataset
ultimate {dict} -- ultimate osc data object
Optional Args:
thresh_low {int} -- oversold signal threshold (default: {30})
thresh_high {int} -- overbought signal threshold (default: {70})
p_bar {ProgressBar} -- (default: {None})
Returns:
dict -- ultimate osc data object
"""
p_bar = kwargs.get('p_bar')
LOW_TH = kwargs.get('thresh_low', 30)
HIGH_TH = kwargs.get('thresh_high', 70)
ult_osc = ultimate['tabular']
trigger = []
marker_val = 0.0
marker_ind = 0
for i, close in enumerate(position['Close']):
# Find bullish signal
if ult_osc[i] < LOW_TH:
ult1 = ult_osc[i]
marker_val = close
marker_ind = i
lows = lower_low(position['Close'], marker_val, marker_ind)
if len(lows) != 0:
ult2 = ult_osc[lows[-1][1]]
if ult2 > ult1:
start_ind = lows[-1][1]
interval = np.max(ult_osc[i:start_ind + 1])
start_ind = bull_bear_th(ult_osc,
start_ind,
interval,
bull_bear='bull')
if start_ind is not None:
trigger.append([
"BULLISH",
date_extractor(position.index[start_ind],
_format='str'),
position['Close'][start_ind], start_ind,
"divergence (original)"
])
# Find bearish signal
if ult_osc[i] > HIGH_TH:
ult1 = ult_osc[i]
marker_val = position['Close'][i]
marker_ind = i
highs = higher_high(position['Close'], marker_val, marker_ind)
if len(highs) != 0:
ult2 = ult_osc[highs[-1][1]]
if ult2 < ult1:
start_ind = highs[-1][1]
interval = np.min(ult_osc[i:start_ind + 1])
start_ind = bull_bear_th(ult_osc,
start_ind,
interval,
bull_bear='bear')
if start_ind is not None:
trigger.append([
"BEARISH",
date_extractor(position.index[start_ind],
_format='str'),
position['Close'][start_ind], start_ind,
"divergence (original)"
])
if p_bar is not None:
p_bar.uptick(increment=0.3)
state = 'n'
prices = [0.0, 0.0]
ults = [0.0, 0.0, 0.0]
for i, ult in enumerate(ult_osc):
# Find bullish divergence and breakout
if (state == 'n') and (ult <= LOW_TH):
state = 'u1'
ults[0] = ult
elif state == 'u1':
if ult < ults[0]:
ults[0] = ult
else:
prices[0] = position['Close'][i - 1]
state = 'u2'
elif (state == 'u2') and (ult > LOW_TH):
state = 'u3'
ults[1] = ult
elif state == 'u3':
if ult > ults[1]:
ults[1] = ult
else:
# we think we've found a divergent high
state = 'u4'
ults[2] = ult
elif state == 'u4':
if ults[2] >= ult:
ults[2] = ult
else:
# We think we've found the bullish 2nd low
prices[1] = position['Close'][i - 1]
state == 'u5'
elif state == 'u5':
if ult > ults[1]:
if prices[0] > prices[1]:
# Bullish breakout!
trigger.append([
"BULLISH",
date_extractor(position.index[start_ind],
_format='str'),
position['Close'][start_ind], start_ind, "divergence"
])
state = 'n'
else:
# False breakout, see if this is the new max:
state = 'u3'
ults[1] = ult
elif ult < ults[2]:
# There may have been a false signal
ults[2] = ult
state = 'u4'
# Find bullish divergence and breakout
if (state == 'n') and (ult >= HIGH_TH):
state = 'e1'
ults[0] = ult
elif state == 'e1':
if ult > ults[0]:
ults[0] = ult
else:
prices[0] = position['Close'][i - 1]
state = 'e2'
elif (state == 'e2') and (ult < HIGH_TH):
state = 'e3'
ults[1] = ult
elif state == 'e3':
if ult < ults[1]:
ults[1] = ult
else:
# we think we've found a divergent low
state = 'e4'
ults[2] = ult
elif state == 'e4':
if ults[2] <= ult:
ults[2] = ult
else:
# We think we've found the bullish 2nd high
prices[1] = position['Close'][i - 1]
state == 'e5'
elif state == 'e5':
if ult < ults[1]:
if prices[0] < prices[1]:
# Bullish breakout!
trigger.append([
"BEARISH",
date_extractor(position.index[start_ind],
_format='str'),
position['Close'][start_ind], start_ind, "divergence"
])
state = 'n'
else:
# False breakout, see if this is the new max:
state = 'e3'
ults[1] = ult
elif ult > ults[2]:
# There may have been a false signal
ults[2] = ult
state = 'e4'
elif ult >= HIGH_TH:
state = 'e1'
ults[0] = ult
elif ult <= LOW_TH:
state = 'u1'
ults[0] = ult
if p_bar is not None:
p_bar.uptick(increment=0.2)
ultimate['indicator'] = trigger
return ultimate