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sua.py
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sua.py
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import numpy as np
import pandas as pd
import datetime as dt
import quantstats as qs
from IPython.display import display
import matplotlib.pyplot as plt
import copy
import yfinance as yf
from fpdf import FPDF
import warnings
#import filterwarnings
# Using Python library with performance and risk statistics commonly used in quantitative finance.
from empyrical import (
cagr,
cum_returns,
stability_of_timeseries,
max_drawdown,
sortino_ratio,
alpha_beta,
tail_ratio,
)
# PyPortfolioOpt is a library that implements portfolio optimization methods, including classical mean-variance optimization techniques and Black-Litterman allocation, as well as more recent developments in the field like shrinkage and Hierarchical Risk Parity.
from pypfopt import (
EfficientFrontier,
risk_models,
expected_returns,
HRPOpt,
objective_functions,
black_litterman,
BlackLittermanModel,
)
# Importing prediction models, metrics and logging
from prophet import Prophet
from darts.models import *
from darts import TimeSeries
from darts.utils.missing_values import fill_missing_values
from darts.metrics import mape, mase
import logging
warnings.filterwarnings("ignore")
TODAY = dt.date.today()
BENCHMARK = ["SPY"]
DAYS_IN_YEAR = 365
rebalance_periods = {
"daily": DAYS_IN_YEAR / 365,
"weekly": DAYS_IN_YEAR / 52,
"monthly": DAYS_IN_YEAR / 12,
"month": DAYS_IN_YEAR / 12,
"m": DAYS_IN_YEAR / 12,
"quarterly": DAYS_IN_YEAR / 4,
"quarter": DAYS_IN_YEAR / 4,
"q": DAYS_IN_YEAR / 4,
"6m": DAYS_IN_YEAR / 2,
"2q": DAYS_IN_YEAR / 2,
"1y": DAYS_IN_YEAR,
"year": DAYS_IN_YEAR,
"y": DAYS_IN_YEAR,
"2y": DAYS_IN_YEAR * 2,
}
class Start:
def __init__(
self,
start_date,
end_date,
portfolio,
weights=None,
rebalance=None,
benchmark=None,
optimizer=None,
max_vol=0.15,
diversification=1,
# confidences=None,
# view=None,
min_weights=None,
max_weights=None,
risk_manager=None,
data=None,
):
if benchmark is None:
benchmark = BENCHMARK
self.start_date = start_date
self.end_date = end_date
self.portfolio = portfolio
self.weights = weights
self.benchmark = benchmark
self.optimizer = optimizer
self.rebalance = rebalance
self.max_vol = max_vol
self.diversification = diversification
self.max_weights = max_weights
self.min_weights = min_weights
self.risk_manager = risk_manager
self.data = data
optimizers = {
"EF": efficient_frontier,
"MEANVAR": mean_var,
"HRP": hrp,
"MINVAR": min_var,
}
if self.optimizer is None and self.weights is None:
self.weights = [1.0 / len(self.portfolio)] * len(self.portfolio)
elif self.optimizer in optimizers.keys():
if self.optimizer == "MEANVAR":
self.weights = optimizers.get(self.optimizer)(
self, vol_max=max_vol, perf=False)
else:
self.weights = optimizers.get(self.optimizer)(self, perf=False)
if self.rebalance is not None:
self.rebalance = make_rebalance(
self.start_date,
self.end_date,
self.optimizer,
self.portfolio,
self.rebalance,
self.weights,
self.max_vol,
self.diversification,
self.min_weights,
self.max_weights,
)
def get_returns(stocks, wts, start_date, end_date):
if len(stocks) > 1:
assets = yf.download(stocks, start=start_date,
end=end_date, progress=False)["Adj Close"]
assets = assets.filter(stocks)
ret_data = assets.pct_change()[1:]
returns = (ret_data * wts).sum(axis=1)
return returns
else:
df = yf.download(stocks, start=start_date,
end=end_date, progress=False)["Adj Close"]
df = pd.DataFrame(df)
returns = df.pct_change()
return returns
def get_returns_from_data(data, wts):
ret_data = data.pct_change()[1:]
returns = (ret_data * wts).sum(axis=1)
return returns
def calculate_information_ratio(returns, benchmark_returns, days=252) -> float:
return_difference = returns - benchmark_returns
volatility = return_difference.std() * np.sqrt(days)
information_ratio_result = return_difference.mean() / volatility
return information_ratio_result
def graph_allocation(my_portfolio):
fig1, ax1 = plt.subplots()
ax1.pie(
my_portfolio.weights,
labels=my_portfolio.portfolio,
autopct="%1.1f%%",
shadow=False,
)
# Equal aspect ratio ensures that pie is drawn as a circle.
ax1.axis("equal")
plt.title("Portfolio's allocation")
plt.show()
def sua(my_portfolio, rf=0.0, sigma_value=1, confidence_value=0.95):
try:
# we want to get the dataframe with the dates and weights
rebalance_schedule = my_portfolio.rebalance
columns = []
for date in rebalance_schedule.columns:
date = date[0:10]
columns.append(date)
rebalance_schedule.columns = columns
# then want to make a list of the dates and start with our first date
dates = [my_portfolio.start_date]
# then our rebalancing dates into that list
dates = dates + rebalance_schedule.columns.to_list()
datess = []
for date in dates:
date = date[0:10]
datess.append(date)
dates = datess
# this will hold returns
returns = pd.Series()
# then we want to be able to call the dates like tuples
for i in range(len(dates) - 1):
# get our weights
weights = rebalance_schedule[str(dates[i + 1])]
# then we want to get the returns
add_returns = get_returns(
my_portfolio.portfolio,
weights,
start_date=dates[i],
end_date=dates[i + 1],
)
# then append those returns
returns = returns.append(add_returns)
except AttributeError:
try:
returns = get_returns_from_data(
my_portfolio.data, my_portfolio.weights)
except AttributeError:
returns = get_returns(
my_portfolio.portfolio,
my_portfolio.weights,
start_date=my_portfolio.start_date,
end_date=my_portfolio.end_date,
)
creturns = (returns + 1).cumprod()
# risk manager
try:
if list(my_portfolio.risk_manager.keys())[0] == "Stop Loss":
values = []
for r in creturns:
if r <= 1 + my_portfolio.risk_manager["Stop Loss"]:
values.append(r)
else:
pass
try:
date = creturns[creturns == values[0]].index[0]
date = str(date.to_pydatetime())
my_portfolio.end_date = date[0:10]
returns = returns[: my_portfolio.end_date]
except Exception as e:
pass
if list(my_portfolio.risk_manager.keys())[0] == "Take Profit":
values = []
for r in creturns:
if r >= 1 + my_portfolio.risk_manager["Take Profit"]:
values.append(r)
else:
pass
try:
date = creturns[creturns == values[0]].index[0]
date = str(date.to_pydatetime())
my_portfolio.end_date = date[0:10]
returns = returns[: my_portfolio.end_date]
except Exception as e:
pass
if list(my_portfolio.risk_manager.keys())[0] == "Max Drawdown":
drawdown = qs.stats.to_drawdown_series(returns)
values = []
for r in drawdown:
if r <= my_portfolio.risk_manager["Max Drawdown"]:
values.append(r)
else:
pass
try:
date = drawdown[drawdown == values[0]].index[0]
date = str(date.to_pydatetime())
my_portfolio.end_date = date[0:10]
returns = returns[: my_portfolio.end_date]
except Exception as e:
pass
except Exception as e:
pass
print("Start date: " + str(my_portfolio.start_date))
print("End date: " + str(my_portfolio.end_date))
benchmark = get_returns(
my_portfolio.benchmark,
wts=[1],
start_date=my_portfolio.start_date,
end_date=my_portfolio.end_date,
)
benchmark = benchmark.dropna()
CAGR = cagr(returns, period='daily', annualization=None)
# CAGR = round(CAGR, 2)
# CAGR = CAGR.tolist()
CAGR = str(round(CAGR * 100, 2)) + "%"
CUM = cum_returns(returns, starting_value=0, out=None) * 100
CUM = CUM.iloc[-1]
CUM = CUM.tolist()
CUM = str(round(CUM, 2)) + "%"
VOL = qs.stats.volatility(returns, annualize=True)
VOL = VOL.tolist()
VOL = str(round(VOL * 100, 2)) + " %"
SR = qs.stats.sharpe(returns, rf=rf)
SR = np.round(SR, decimals=2)
SR = str(SR)
sua.SR = SR
CR = qs.stats.calmar(returns)
CR = CR.tolist()
CR = str(round(CR, 2))
sua.CR = CR
STABILITY = stability_of_timeseries(returns)
STABILITY = round(STABILITY, 2)
STABILITY = str(STABILITY)
MD = max_drawdown(returns, out=None)
MD = str(round(MD * 100, 2)) + " %"
"""OR = omega_ratio(returns, risk_free=0.0, required_return=0.0)
OR = round(OR,2)
OR = str(OR)
print(OR)"""
SOR = sortino_ratio(returns, required_return=0, period='daily')
SOR = round(SOR, 2)
SOR = str(SOR)
SK = qs.stats.skew(returns)
SK = round(SK, 2)
SK = SK.tolist()
SK = str(SK)
KU = qs.stats.kurtosis(returns)
KU = round(KU, 2)
KU = KU.tolist()
KU = str(KU)
TA = tail_ratio(returns)
TA = round(TA, 2)
TA = str(TA)
CSR = qs.stats.common_sense_ratio(returns)
CSR = round(CSR, 2)
CSR = CSR.tolist()
CSR = str(CSR)
VAR = qs.stats.value_at_risk(
returns, sigma=sigma_value, confidence=confidence_value
)
VAR = np.round(VAR, decimals=2)
VAR = str(VAR * 100) + " %"
alpha, beta = alpha_beta(returns, benchmark, risk_free=rf)
AL = round(alpha, 2)
BTA = round(beta, 2)
def condition(x):
return x > 0
win = sum(condition(x) for x in returns)
total = len(returns)
win_ratio = win / total
win_ratio = win_ratio * 100
win_ratio = round(win_ratio, 2)
IR = calculate_information_ratio(returns, benchmark.iloc[:, 0])
IR = round(IR, 2)
data = {
"": [
"Annual return",
"Cumulative return",
"Annual volatility",
"Winning day ratio",
"Sharpe ratio",
"Calmar ratio",
"Information ratio",
"Stability",
"Max Drawdown",
"Sortino ratio",
"Skew",
"Kurtosis",
"Tail Ratio",
"Common sense ratio",
"Daily value at risk",
"Alpha",
"Beta",
],
"Backtest": [
CAGR,
CUM,
VOL,
f"{win_ratio}%",
SR,
CR,
IR,
STABILITY,
MD,
SOR,
SK,
KU,
TA,
CSR,
VAR,
AL,
BTA,
],
}
# Create DataFrame
df = pd.DataFrame(data)
df.set_index("", inplace=True)
df.style.set_properties(
**{"background-color": "white", "color": "black", "border-color": "black"}
)
display(df)
sua.df = data
y = []
for x in returns:
y.append(x)
arr = np.array(y)
# arr
# returns.index
my_color = np.where(arr >= 0, "blue", "grey")
plt.figure(figsize=(30, 8))
plt.vlines(x=returns.index, ymin=0, ymax=arr, color=my_color, alpha=0.4)
plt.title("Returns")
sua.returns = returns
sua.creturns = creturns
sua.benchmark = benchmark
sua.CAGR = CAGR
sua.CUM = CUM
sua.VOL = VOL
sua.SR = SR
sua.win_ratio = win_ratio
sua.CR = CR
sua.IR = IR
sua.STABILITY = STABILITY
sua.MD = MD
sua.SOR = SOR
sua.SK = SK
sua.KU = KU
sua.TA = TA
sua.CSR = CSR
sua.VAR = VAR
sua.AL = AL
sua.BTA = BTA
try:
sua.orderbook = make_rebalance.output
except Exception as e:
OrderBook = pd.DataFrame(
{
"Assets": my_portfolio.portfolio,
"Allocation": my_portfolio.weights,
}
)
sua.orderbook = OrderBook.T
wts = copy.deepcopy(my_portfolio.weights)
indices = [i for i, x in enumerate(wts) if x == 0.0]
while 0.0 in wts:
wts.remove(0.0)
for i in sorted(indices, reverse=True):
del my_portfolio.portfolio[i]
return (
qs.plots.returns(returns, benchmark, cumulative=True),
qs.plots.yearly_returns(returns, benchmark),
qs.plots.monthly_heatmap(returns),
qs.plots.drawdown(returns),
qs.plots.drawdowns_periods(returns),
qs.plots.rolling_volatility(returns),
qs.plots.rolling_sharpe(returns),
qs.plots.rolling_beta(returns, benchmark),
graph_opt(my_portfolio.portfolio, wts, pie_size=7, font_size=14),
)
def flatten(subject) -> list:
muster = []
for item in subject:
if isinstance(item, (list, tuple, set)):
muster.extend(flatten(item))
else:
muster.append(item)
return muster
def graph_opt(my_portfolio, my_weights, pie_size, font_size):
fig1, ax1 = plt.subplots()
fig1.set_size_inches(pie_size, pie_size)
cs = [
"#ff9999",
"#66b3ff",
"#99ff99",
"#ffcc99",
"#f6c9ff",
"#a6fff6",
"#fffeb8",
"#ffe1d4",
"#cccdff",
"#fad6ff",
]
ax1.pie(my_weights, labels=my_portfolio,
autopct="%1.1f%%", shadow=False, colors=cs)
# Equal aspect ratio ensures that pie is drawn as a circle.
ax1.axis("equal")
plt.rcParams["font.size"] = font_size
plt.show()
def equal_weighting(my_portfolio) -> list:
return [1.0 / len(my_portfolio.portfolio)] * len(my_portfolio.portfolio)
def efficient_frontier(my_portfolio, perf=True) -> list:
# changed to take in desired timeline, the problem is that it would use all historical data
ohlc = yf.download(
my_portfolio.portfolio,
start=my_portfolio.start_date,
end=my_portfolio.end_date,
progress=False,
)
prices = ohlc["Adj Close"].dropna(how="all")
df = prices.filter(my_portfolio.portfolio)
# sometimes we will pick a date range where company isn't public we can't set price to 0 so it has to go to 1
df = df.fillna(1)
mu = expected_returns.mean_historical_return(df)
S = risk_models.sample_cov(df)
# optimize for max sharpe ratio
ef = EfficientFrontier(mu, S)
if my_portfolio.min_weights is not None:
ef.add_constraint(lambda x: x >= my_portfolio.min_weights)
if my_portfolio.max_weights is not None:
ef.add_constraint(lambda x: x <= my_portfolio.max_weights)
weights = ef.max_sharpe()
cleaned_weights = ef.clean_weights()
wts = cleaned_weights.items()
result = []
for val in wts:
a, b = map(list, zip(*[val]))
result.append(b)
if perf is True:
pred = ef.portfolio_performance(verbose=True)
return flatten(result)
def hrp(my_portfolio, perf=True) -> list:
# changed to take in desired timeline, the problem is that it would use all historical data
ohlc = yf.download(
my_portfolio.portfolio,
start=my_portfolio.start_date,
end=my_portfolio.end_date,
progress=False,
)
prices = ohlc["Adj Close"].dropna(how="all")
prices = prices.filter(my_portfolio.portfolio)
# sometimes we will pick a date range where company isn't public we can't set price to 0 so it has to go to 1
prices = prices.fillna(1)
rets = expected_returns.returns_from_prices(prices)
hrp = HRPOpt(rets)
hrp.optimize()
weights = hrp.clean_weights()
wts = weights.items()
result = []
for val in wts:
a, b = map(list, zip(*[val]))
result.append(b)
if perf is True:
hrp.portfolio_performance(verbose=True)
return flatten(result)
def mean_var(my_portfolio, vol_max=0.15, perf=True) -> list:
# changed to take in desired timeline, the problem is that it would use all historical data
ohlc = yf.download(
my_portfolio.portfolio,
start=my_portfolio.start_date,
end=my_portfolio.end_date,
progress=False,
)
prices = ohlc["Adj Close"].dropna(how="all")
prices = prices.filter(my_portfolio.portfolio)
# sometimes we will pick a date range where company isn't public we can't set price to 0 so it has to go to 1
prices = prices.fillna(1)
mu = expected_returns.capm_return(prices)
S = risk_models.CovarianceShrinkage(prices).ledoit_wolf()
ef = EfficientFrontier(mu, S)
ef.add_objective(objective_functions.L2_reg,
gamma=my_portfolio.diversification)
if my_portfolio.min_weights is not None:
ef.add_constraint(lambda x: x >= my_portfolio.min_weights)
if my_portfolio.max_weights is not None:
ef.add_constraint(lambda x: x <= my_portfolio.max_weights)
ef.efficient_risk(vol_max)
weights = ef.clean_weights()
wts = weights.items()
result = []
for val in wts:
a, b = map(list, zip(*[val]))
result.append(b)
if perf is True:
ef.portfolio_performance(verbose=True)
return flatten(result)
def min_var(my_portfolio, perf=True) -> list:
ohlc = yf.download(
my_portfolio.portfolio,
start=my_portfolio.start_date,
end=my_portfolio.end_date,
progress=False,
)
prices = ohlc["Adj Close"].dropna(how="all")
prices = prices.filter(my_portfolio.portfolio)
mu = expected_returns.capm_return(prices)
S = risk_models.CovarianceShrinkage(prices).ledoit_wolf()
ef = EfficientFrontier(mu, S)
ef.add_objective(objective_functions.L2_reg,
gamma=my_portfolio.diversification)
if my_portfolio.min_weights is not None:
ef.add_constraint(lambda x: x >= my_portfolio.min_weights)
if my_portfolio.max_weights is not None:
ef.add_constraint(lambda x: x <= my_portfolio.max_weights)
ef.min_volatility()
weights = ef.clean_weights()
wts = weights.items()
result = []
for val in wts:
a, b = map(list, zip(*[val]))
result.append(b)
if perf is True:
ef.portfolio_performance(verbose=True)
return flatten(result)
def optimize_portfolio(my_portfolio, vol_max=25, pie_size=5, font_size=14):
returns1 = get_returns(
my_portfolio.portfolio,
equal_weighting(my_portfolio),
start_date=my_portfolio.start_date,
end_date=my_portfolio.end_date,
)
creturns1 = (returns1 + 1).cumprod()
port = copy.deepcopy(my_portfolio.portfolio)
wts = [1.0 / len(my_portfolio.portfolio)] * len(my_portfolio.portfolio)
optimizers = {
"EF": efficient_frontier,
"MEANVAR": mean_var,
"HRP": hrp,
"MINVAR": min_var,
}
if my_portfolio.optimizer in optimizers.keys():
if my_portfolio.optimizer == "MEANVAR":
wts = optimizers.get(my_portfolio.optimizer)(
my_portfolio, my_portfolio.max_vol)
else:
wts = optimizers.get(my_portfolio.optimizer)(my_portfolio)
else:
opt = my_portfolio.optimizer
my_portfolio.weights = opt()
print(wts)
print("\n")
indices = [i for i, x in enumerate(wts) if x == 0.0]
while 0.0 in wts:
wts.remove(0.0)
for i in sorted(indices, reverse=True):
del port[i]
graph_opt(port, wts, pie_size, font_size)
print("\n")
returns2 = get_returns(
port, wts, start_date=my_portfolio.start_date, end_date=my_portfolio.end_date
)
creturns2 = (returns2 + 1).cumprod()
plt.rcParams["font.size"] = 13
plt.figure(figsize=(30, 10))
plt.xlabel("Portfolio vs Benchmark")
ax1 = creturns1.plot(color="blue", label="Without optimization")
ax2 = creturns2.plot(color="red", label="With optimization")
h1, l1 = ax1.get_legend_handles_labels()
h2, l2 = ax2.get_legend_handles_labels()
plt.legend(l1 + l2, loc=2)
plt.show()
def check_schedule(rebalance) -> bool:
valid_schedule = False
if rebalance.lower() in rebalance_periods.keys():
valid_schedule = True
return valid_schedule
def valid_range(start_date, end_date, rebalance) -> tuple:
# make the start date to a datetime
start_date = dt.datetime.strptime(start_date, "%Y-%m-%d")
# have to make end date a datetime because strptime is not supported for date
end_date = dt.datetime.strptime(end_date, '%Y-%m-%d')
# gets the number of days
days = (end_date - start_date).days
# checking that date range covers rebalance period
if rebalance in rebalance_periods.keys() and days <= (int(rebalance_periods[rebalance])):
raise KeyError("Date Range does not encompass rebalancing interval")
# we will needs these dates later on so we'll return them back
return start_date, end_date
def get_date_range(start_date, end_date, rebalance) -> list:
# this will keep track of the rebalancing dates and we want to start on the first date
rebalance_dates = [start_date]
input_date = start_date
if rebalance in rebalance_periods.keys():
# run for an arbitrarily large number we'll resolve this by breaking when we break the equality
for i in range(1000):
# increment the date based on the selected period
input_date = input_date + \
dt.timedelta(days=rebalance_periods.get(rebalance))
if input_date <= end_date:
# append the new date if it is earlier or equal to the final date
rebalance_dates.append(input_date)
else:
# break when the next rebalance date is later than our end date
break
# then we want to return those dates
return rebalance_dates
def make_rebalance(
start_date,
end_date,
optimize,
portfolio_input,
rebalance,
allocation,
vol_max,
div,
min,
max,
) -> pd.DataFrame:
sdate = str(start_date)[:10]
if rebalance[0] != sdate:
# makes sure that the value passed through for rebalancing is a valid one
valid_schedule = check_schedule(rebalance)
if valid_schedule is False:
raise KeyError("Not an accepted rebalancing schedule")
# this checks to make sure that the date range given works for the rebalancing
start_date, end_date = valid_range(start_date, end_date, rebalance)
# this function will get us the specific dates
if rebalance[0] != sdate:
dates = get_date_range(start_date, end_date, rebalance)
else:
dates = rebalance
# we are going to make columns with the end date and the weights
columns = ["end_date"] + portfolio_input
# then make a dataframe with the index being the tickers
output_df = pd.DataFrame(index=portfolio_input)
for i in range(len(dates) - 1):
try:
portfolio = Start(
start_date=dates[0],
end_date=dates[i + 1],
portfolio=portfolio_input,
weights=allocation,
optimizer="{}".format(optimize),
max_vol=vol_max,
diversification=div,
min_weights=min,
max_weights=max,
)
except TypeError:
portfolio = Start(
start_date=dates[0],
end_date=dates[i + 1],
portfolio=portfolio_input,
weights=allocation,
optimizer=optimize,
max_vol=vol_max,
diversification=div,
min_weights=min,
max_weights=max,
)
output_df["{}".format(dates[i + 1])] = portfolio.weights
# we have to run it one more time to get what the optimization is for up to today's date
try:
portfolio = Start(
start_date=dates[0],
portfolio=portfolio_input,
weights=allocation,
optimizer="{}".format(optimize),
max_vol=vol_max,
diversification=div,
min_weights=min,
max_weights=max,
)
except TypeError:
portfolio = Start(
start_date=dates[0],
portfolio=portfolio_input,
weights=allocation,
optimizer=optimize,
max_vol=vol_max,
diversification=div,
min_weights=min,
max_weights=max,
)
output_df["{}".format(TODAY)] = portfolio.weights
make_rebalance.output = output_df
return output_df
def get_report(my_portfolio, rf=0.0, sigma_value=1, confidence_value=0.95, filename: str = "report.pdf"):
try:
# we want to get the dataframe with the dates and weights
rebalance_schedule = my_portfolio.rebalance
columns = []
for date in rebalance_schedule.columns:
date = date[0:10]
columns.append(date)
rebalance_schedule.columns = columns
# then want to make a list of the dates and start with our first date
dates = [my_portfolio.start_date]
# then our rebalancing dates into that list
dates = dates + rebalance_schedule.columns.to_list()
datess = []
for date in dates:
date = date[0:10]
datess.append(date)
dates = datess
# this will hold returns
returns = pd.Series()
# then we want to be able to call the dates like tuples
for i in range(len(dates) - 1):
# get our weights
weights = rebalance_schedule[str(dates[i + 1])]
# then we want to get the returns
add_returns = get_returns(
my_portfolio.portfolio,
weights,
start_date=dates[i],
end_date=dates[i + 1],
)
# then append those returns
returns = returns.append(add_returns)
except AttributeError:
try:
returns = get_returns_from_data(
my_portfolio.data, my_portfolio.weights)
except AttributeError:
returns = get_returns(
my_portfolio.portfolio,
my_portfolio.weights,
start_date=my_portfolio.start_date,
end_date=my_portfolio.end_date,
)
creturns = (returns + 1).cumprod()
# risk manager
try:
if list(my_portfolio.risk_manager.keys())[0] == "Stop Loss":
values = []
for r in creturns:
if r <= 1 + my_portfolio.risk_manager["Stop Loss"]:
values.append(r)
else:
pass
try:
date = creturns[creturns == values[0]].index[0]
date = str(date.to_pydatetime())
my_portfolio.end_date = date[0:10]
returns = returns[: my_portfolio.end_date]
except Exception as e:
pass
if list(my_portfolio.risk_manager.keys())[0] == "Take Profit":
values = []
for r in creturns:
if r >= 1 + my_portfolio.risk_manager["Take Profit"]:
values.append(r)
else:
pass
try:
date = creturns[creturns == values[0]].index[0]
date = str(date.to_pydatetime())
my_portfolio.end_date = date[0:10]
returns = returns[: my_portfolio.end_date]
except Exception as e:
pass
if list(my_portfolio.risk_manager.keys())[0] == "Max Drawdown":
drawdown = qs.stats.to_drawdown_series(returns)
values = []
for r in drawdown:
if r <= my_portfolio.risk_manager["Max Drawdown"]: