def generate_analysis(fund: pd.DataFrame, x_list: list, y_list: list,
len_list: list, color_list: list) -> list:
"""Generate Analysis
Arguments:
fund {pd.DataFrame} -- fund dataset
x_list {list} -- list of x-value lists
y_list {list} -- list of y-value lists
len_list {list} -- list of trendline lengths
color_list {list} -- list of colors, associated with each trendline
Returns:
list -- list of analysis data objects
"""
analysis = []
for i, x in enumerate(x_list):
sub = {}
sub['length'] = len(x)
sub['color'] = color_list[i]
reg = linregress(x[0:3], y=y_list[i][0:3])
sub['slope'] = reg[0]
sub['intercept'] = reg[1]
sub['start'] = {}
sub['start']['index'] = x[0]
sub['start']['date'] = fund.index[x[0]].strftime("%Y-%m-%d")
sub['end'] = {}
sub['end']['index'] = x[len(x) - 1]
sub['end']['date'] = fund.index[x[len(x) - 1]].strftime("%Y-%m-%d")
sub['term'] = len_list[i]
if sub['slope'] < 0:
sub['type'] = 'bear'
else:
sub['type'] = 'bull'
sub['x'] = {}
sub['x']['by_date'] = dates_convert_from_index(fund, [x], to_str=True)
sub['x']['by_index'] = x
if sub['end']['index'] == len(fund['Close']) - 1:
sub['current'] = True
else:
sub['current'] = False
sub = attribute_analysis(fund, x, y_list[i], sub)
analysis.append(sub)
return analysis
def find_resistance_support_lines(data: pd.DataFrame, **kwargs) -> dict:
"""Find Resistance / Support Lines
Arguments:
data {pd.DataFrame} -- fund historical data
Optional Args:
name {str} -- name of fund, primarily for plotting (default: {''})
plot_output {bool} -- True to render plot in realtime (default: {True})
timeframes {list} -- time windows for feature discovery (default: {[13, 21, 34, 55]})
progress_bar {ProgressBar} -- (default: {None})
view {str} -- directory of plots (default: {''})
Returns:
dict -- contains all trendline information
"""
name = kwargs.get('name', '')
plot_output = kwargs.get('plot_output', True)
timeframes = kwargs.get('timeframes', [13, 21, 34, 55])
progress_bar = kwargs.get('progress_bar', None)
view = kwargs.get('view', '')
resist_support_lines = {}
resist_support_lines['support'] = {}
resist_support_lines['resistance'] = {}
increment = 0.5 / (float(len(timeframes)))
support = {}
resistance = {}
for time in timeframes:
support[str(time)] = {}
x, y = find_points(data, line_type='support',
timeframe=time, filter_type='windowed')
support[str(time)]['x'] = x
support[str(time)]['y'] = y
sorted_support = sort_and_group(support)
resist_support_lines['support'][str(
time)] = cluster_notables(sorted_support, data)
resistance[str(time)] = {}
x2, y2 = find_points(data, line_type='resistance', timeframe=time)
resistance[str(time)]['x'] = x2
resistance[str(time)]['y'] = y2
sorted_resistance = sort_and_group(resistance)
resist_support_lines['resistance'][str(
time)] = cluster_notables(sorted_resistance, data)
if progress_bar is not None:
progress_bar.uptick(increment=increment)
Xs, Ys, Xr, Yr = get_plot_content(
data, resist_support_lines, selected_timeframe=str(timeframes[len(timeframes)-1]))
if progress_bar is not None:
progress_bar.uptick(increment=0.2)
Xc, Yc = res_sup_unions(Yr, Xr, Ys, Xs)
# Odd list behavior when no res/sup lines drawn on appends, so if-else to fix
if len(Yc) > 0:
Xp = Xc.copy()
Xp2 = dates_convert_from_index(data, Xp)
Yp = Yc.copy()
Xp2.append(data.index)
Yp.append(remove_dates_from_close(data))
else:
Xp2 = data.index
Yp = [remove_dates_from_close(data)]
c = colorize_plots(len(Yp), primary_plot_index=len(Yp)-1)
if progress_bar is not None:
progress_bar.uptick(increment=0.1)
name2 = INDEXES.get(name, name)
if plot_output:
generic_plotting(Yp, x=Xp2, colors=c,
title=f'{name2} Major Resistance & Support')
else:
filename = f"{name}/{view}/resist_support_{name}.png"
generic_plotting(
Yp, x=Xp2, colors=c, title=f'{name2} Major Resistance & Support', saveFig=True, filename=filename)
if progress_bar is not None:
progress_bar.uptick(increment=0.1)
analysis = detailed_analysis([Yr, Ys, Yc], data, key_args={'Colors': c})
if progress_bar is not None:
progress_bar.uptick(increment=0.1)
analysis['type'] = 'trend'
return analysis
def get_trendlines(fund: pd.DataFrame, **kwargs) -> dict:
"""Get Trendlines
Arguments:
fund {pd.DataFrame} -- fund historical data
Optional Args:
name {str} -- name of fund, primarily for plotting (default: {''})
plot_output {bool} -- True to render plot in realtime (default: {True})
interval {list} -- list of windowed filter time periods (default: {[4, 8, 16, 32]})
progress_bar {ProgressBar} -- (default: {None})
sub_name {str} -- file extension within 'name' directory (default: {name})
view {str} -- directory of plots (default: {''})
meta {dict} -- 'metadata' object for fund (default: {None})
out_suppress {bool} -- if True, skips plotting (default: {False})
trend_window {list} -- line time windows (default: {[163, 91, 56, 27]})
Returns:
trends {dict} -- contains all trend lines determined by algorithm
"""
name = kwargs.get('name', '')
plot_output = kwargs.get('plot_output', True)
interval = kwargs.get('interval', [4, 8, 16, 32])
progress_bar = kwargs.get('progress_bar', None)
sub_name = kwargs.get('sub_name', f"trendline_{name}")
view = kwargs.get('view', '')
meta = kwargs.get('meta')
out_suppress = kwargs.get('out_suppress', False)
trend_window = kwargs.get('trend_window', [163, 91, 56, 27])
# Not ideal to ignore warnings, but these are handled already by scipy/numpy so... eh...
warnings.filterwarnings("ignore", category=RuntimeWarning)
trends = dict()
mins_y = []
mins_x = []
maxes_y = []
maxes_x = []
all_x = []
vq = 0.06
if meta is not None:
vol = meta.get('volatility', {}).get('VQ')
if vol is not None:
vq = vol / 100.0
increment = 0.7 / (float(len(interval)) * 3)
for i, period in enumerate(interval):
ma_size = period
# ma = windowed_ma_list(fund['Close'], interval=ma_size)
weight_strength = 2.0 + (0.1 * float(i))
ma = windowed_moving_avg(fund['Close'],
interval=ma_size,
weight_strength=weight_strength,
data_type='list',
filter_type='exponential')
ex = find_filtered_local_extrema(ma)
r = reconstruct_extrema(fund,
extrema=ex,
ma_size=ma_size,
ma_type='windowed')
# Cleanse data sample for duplicates and errors
r = remove_duplicates(r, method='point')
for y in r['min']:
if y[0] not in mins_x:
mins_x.append(y[0])
mins_y.append(y[1])
if y[0] not in all_x:
all_x.append(y[0])
for y in r['max']:
if y[0] not in maxes_x:
maxes_x.append(y[0])
maxes_y.append(y[1])
if y[0] not in all_x:
all_x.append(y[0])
if progress_bar is not None:
progress_bar.uptick(increment=increment)
zipped_min = list(zip(mins_x, mins_y))
zipped_min.sort(key=lambda x: x[0])
mins_x = [x[0] for x in zipped_min]
mins_y = [y[1] for y in zipped_min]
zipped_max = list(zip(maxes_x, maxes_y))
zipped_max.sort(key=lambda x: x[0])
maxes_x = [x[0] for x in zipped_max]
maxes_y = [y[1] for y in zipped_max]
# mins_xd = [fund.index[x] for x in mins_x]
# maxes_xd = [fund.index[x] for x in maxes_x]
long_term = trend_window[0]
intermediate_term = trend_window[1]
short_term = trend_window[2]
near_term = trend_window[3]
X0, Y0 = get_lines_from_period(fund,
[mins_x, mins_y, maxes_x, maxes_y, all_x],
interval=long_term,
vq=vq)
X1, Y1 = get_lines_from_period(fund,
[mins_x, mins_y, maxes_x, maxes_y, all_x],
interval=intermediate_term,
vq=vq)
X2, Y2 = get_lines_from_period(fund,
[mins_x, mins_y, maxes_x, maxes_y, all_x],
interval=short_term,
vq=vq)
X3, Y3 = get_lines_from_period(fund,
[mins_x, mins_y, maxes_x, maxes_y, all_x],
interval=near_term,
vq=vq)
if progress_bar is not None:
progress_bar.uptick(increment=increment * 4.0)
X = []
Y = []
C = []
L = []
for i, x in enumerate(X0):
X.append(x)
Y.append(Y0[i])
C.append('blue')
L.append('long')
for i, x in enumerate(X1):
X.append(x)
Y.append(Y1[i])
C.append('green')
L.append('intermediate')
for i, x in enumerate(X2):
X.append(x)
Y.append(Y2[i])
C.append('orange')
L.append('short')
for i, x in enumerate(X3):
X.append(x)
Y.append(Y3[i])
C.append('red')
L.append('near')
if progress_bar is not None:
progress_bar.uptick(increment=increment * 4.0)
analysis_list = generate_analysis(fund,
x_list=X,
y_list=Y,
len_list=L,
color_list=C)
if progress_bar is not None:
progress_bar.uptick(increment=0.1)
X = dates_convert_from_index(fund, X)
X.append(fund.index)
Y.append(fund['Close'])
C.append('black')
name2 = INDEXES.get(name, name)
if not out_suppress:
try:
title = f"{name2} Trend Lines for {near_term}, {short_term}, " + \
f"{intermediate_term}, and {long_term} Periods"
if plot_output:
generic_plotting(Y, x=X, colors=C, title=title)
else:
filename = os.path.join(name, view, f"{sub_name}.png")
generic_plotting(Y,
x=X,
colors=C,
title=title,
saveFig=True,
filename=filename)
except:
print(
f"{WARNING}Warning: plot failed to generate in trends.get_trendlines.{NORMAL}"
)
if progress_bar is not None:
progress_bar.uptick(increment=0.2)
trends['trendlines'] = analysis_list
current = []
metrics = []
for trend in analysis_list:
if trend['current']:
current.append(trend)
met = {f"{trend.get('term')} term": trend.get('type')}
met['periods'] = trend.get('length')
metrics.append(met)
trends['current'] = current
trends['metrics'] = metrics
trends['type'] = 'trend'
return trends