def show_yield(ticker, start=None, end=None, weeks=52):
"""
Calculate annualized return. Simple return is calculated
input: ticker ticker of the stock/ETF
start start date, default '2000-01-01'
end end date, default current
weeks number of weeks for each calculation, default 52
Return a DataFrame with three rows: Adj Close, Dividend, Close
"""
from invest.useful import convert_time
from invest.get_data import read_ETF
from invest.calculation import add_dividend, get_returns
from time_series.functions import resample
start, end = convert_time(start, end)
data = read_ETF(ticker)[start:end]
add_dividend(data, price='Close', adj='Adj Close', out='Dividend')
data['Dividend'] = np.cumsum(data.Dividend)
weekly = resample(data, style='week', method='close')
weekly = weekly[(weekly.shape[0] - 1) % weeks::weeks]
df = get_returns(weekly[['Adj Close', 'Dividend', 'Close']], 'simple')
df['Dividend'] = (weekly.Dividend.diff() / weekly.Close.shift(1))
df = df * 100 * 52 / weeks
from datetime import timedelta
ds = df.index
xlim = [
ds[0] - timedelta(days=3 * weeks), ds[-1] + timedelta(days=3 * weeks)
]
plt.figure(figsize=(14, 3))
plt.title("Annualized Return")
plt.hlines(xmin=xlim[0], xmax=xlim[1], y=0)
plt.hlines(xmin=xlim[0],
xmax=xlim[1],
y=df['Adj Close'].mean(),
linestyle='--',
color='#1f77b4')
plt.hlines(xmin=xlim[0],
xmax=xlim[1],
y=df.Dividend.mean(),
linestyle='--',
color='#ff7f0e')
plt.bar(ds, df.Close, width=5 * weeks, label='Yield')
plt.bar(ds,
df.Dividend,
bottom=df.Close,
width=5 * weeks,
label='Div_Yield')
plt.plot(ds, df['Adj Close'], 'o-', label='Adj_Yield')
plt.xlabel("Date to sell")
plt.xlim(xlim)
plt.ylim([np.min(df.values) - 0.2, np.max(df.values) + 0.2])
plt.legend(bbox_to_anchor=(1.01, 0.9), loc='upper left')
plt.grid()
df.index = df.index.date
print(np.round(df, 2))
plt.show()
return np.round(df, 2).T
def show_trend(ticker, start=None, end=None):
"""
Plot price change, return and volatility
input: ticker ticker of the stock/ETF
start start date, default '2000-01-01'
end end date, default current
Return correlation between return and volatility
"""
from invest.useful import convert_time
from invest.get_data import read_ETF
from invest.calculation import get_returns
from time_series.functions import moving_agg, resample
from basic.mathe import covariance, correlation
start, end = convert_time(start, end)
data = read_ETF(ticker)[start:end]
fig = plt.figure(figsize=(14,6))
fig.add_axes([0.05,0.68,0.94,0.3])
for c in ['Close','Adj Close']:
plt.plot(data.index, data[c], label=c)
plt.xlim(data.index[0],data.index[-1])
plt.xticks([])
plt.ylabel("Price ($)")
plt.legend(loc='best')
weekly = resample(data, style='week', method='close')
df = get_returns(weekly.Close, 'simple')
fig.add_axes([0.05,0.38,0.94,0.3])
m = moving_agg(df, window=52, step=1, func=np.sum)
plt.plot(df.index[51:], m*100)
plt.hlines(xmin=data.index[0], xmax=data.index[-1], y=0, linestyle='--')
plt.xlim(data.index[0],data.index[-1])
plt.xticks([])
plt.ylabel("Annual Return (%)")
plt.legend(loc='best')
fig.add_axes([0.05,0.08,0.94,0.3])
v = moving_agg(df, window=52, step=1, func=covariance)
v = np.sqrt(v*52)
plt.plot(df.index[51:], v*100)
plt.xlim(data.index[0],data.index[-1])
plt.ylabel("Volatility (%)")
plt.gca().set_ylim(bottom=0)
plt.legend(loc='best')
corr = correlation(m, v)
print("Correlation between return and volatility:", corr)
plt.show()
return corr
def generate(tickers, features):
"""
input: tickers a list of tickers of stocks/ETFs
features a list of feature names to be calculated
Return a pandas DataFrame
"""
from invest.get_data import read_ETF
from time_series.functions import resample
from invest.calculation import get_returns
data = pd.DataFrame()
for t in tickers:
etf = read_ETF(t)
weekly = resample(etf, style='week', method='close')
ret = get_returns(weekly, style='simple')
rv = get_return_vol(ret, scale=52, ret=True, plotit=False)
def set_benchmark(self):
ticker = self.benchmark.get().upper()
if ticker == "":
self._logger_.info("Remove benchmark")
self._data_['benchmark'] = 0
else:
self._logger_.info("Set benchmark as {}".format(ticker))
try:
data = read_ETF(ticker, file_dir=path + "\\data_temp")
except:
self._logger_.error("Cannot load benchmark {}".format(ticker))
else:
self._data_['benchmark'] = get_returns(resample(
data.Close, column=None, style="week", method='close'),
style='simple',
fillna=False)
self.update_plot()
def initial_plot(self, column='Adj Close', style='week', start='2015-1-1'):
self._logger_.debug("Initialize plots for portfolio window")
tickers = self.select.get_right()
self._data_ = get_returns(resample(read_portfolio(tickers,
column=column,
start=start),
column=None,
style=style,
method='close'),
style='simple',
fillna=False)
self._data_['benchmark'] = 0
data = self._data_.iloc[-52:, :-1].dropna(axis=1, how='any')
rv = get_return_vol(pd.concat([data * 3, -data], axis=1),
scale=52,
ret=True,
plotit=False)
fig = return_vol(rv.Return, rv.Volatility, rv.index)
fig.axes[0].plot([0], [0], 'r*')
return fig, pie_plot([10, 6], labels=['a', 'b'])
def holdings_performance(file, frame=None):
"""
Calculate performance of current holdings.
input: file a csv file containing all transactions
columns are Date, Ticker, Shares, Price
frame a tkinter frame to show results. Default None
"""
logger = logging.getLogger(__name__)
try:
holdings = pd.read_csv(file, index_col=None, parse_dates=[0]).dropna()
except Exception as err:
logger.error("Cannot open holdings file {}".format(file))
logger.error(err)
return
logger.debug("input transactions:")
logger.debug("{} \t{}\t{}\t{}".format(*holdings.columns))
for i in range(holdings.shape[0]):
logger.debug("{} \t{} \t{} \t{}".format(holdings.iloc[i, 0].date(),
*holdings.iloc[i, 1:]))
tickers = holdings.Ticker.unique()
if len(tickers) == 0:
logger.info("No data provided")
return
from invest.get_data import get_latest_ETFs
first_date = holdings.Date.min() - timedelta(days=7)
data = get_latest_ETFs(tickers, start=first_date)
from invest.calculation import add_dividend
for t in tickers:
add_dividend(data,
price=('Close', t),
adj=('Adj Close', t),
out=('Dividend', t))
columns = [
"Ticker", "Buy Date", "Buy Price", "Current Price", "Buy Shares",
"Reinvested Shares", "Capital Gain", "Dividend Gain", "Total Gain"
]
output = pd.DataFrame(columns=columns, index=holdings.index)
output['Ticker'] = holdings.Ticker.values
output['Buy Date'] = holdings.Date.dt.date.values
output['Buy Price'] = holdings.Price.values
for t in tickers:
index = output.index[output.Ticker == t]
output.loc[index, 'Current Price'] = data['Close'][t][-1]
# calculate reinvested value
# settlement date is two business days after buy date
dates = holdings.Date[index]
loc = np.array([data.index.get_loc(x) for x in dates]) + 2
loc = np.clip(loc, 0, data.shape[0] - 1)
output.loc[index, 'Close'] = data['Close'][t].iloc[loc].values
output.loc[index, 'Adj Close'] = data['Adj Close'][t].iloc[loc].values
output.loc[index, 'Dividend'] = [
data.Dividend[t].iloc[d:].sum() for d in loc
]
output['Current Price'] = output['Current Price'].astype(float)
output['Buy Shares'] = holdings.Shares.values
current_share = output['Close'] / output['Adj Close'] * output['Buy Shares']
output['Reinvested Shares'] = np.round(
current_share - output['Buy Shares'], 5)
output['Value'] = current_share * output['Current Price']
days = (date.today() - output['Buy Date']).dt.days / 365
output['Capital Gain'] = (output['Current Price'] -
output['Buy Price']) * output['Buy Shares']
output['Capital Gain %'] = (output['Current Price'] / output['Buy Price'] -
1) / days * 100
output['Dividend Gain'] = output['Dividend'] * output['Buy Shares']
output['Dividend Gain %'] = output['Dividend'] / output[
'Buy Price'] / days * 100
output['Total Gain'] = output['Current Price'] * current_share - output[
'Buy Price'] * output['Buy Shares']
out = file.replace('/', '\\').split('\\')[:-1]
out.append("output.csv")
out = '\\'.join(out)
logger.info("Detailed investment summary saved in file {}".format(out))
output.to_csv(out, index=False)
summary = output.groupby('Ticker')['Value', 'Capital Gain',
'Dividend Gain', 'Total Gain'].sum()
summary['Dividend Gain'] = np.round(
summary['Total Gain'] - summary['Capital Gain'], 2)
summary['Value'] = np.round(summary['Value'], 2)
summary['Capital Gain'] = np.round(summary['Capital Gain'], 2)
summary['Total Gain'] = np.round(summary['Total Gain'], 2)
from time_series.functions import resample
from invest.calculation import get_returns
weekly = resample(data, style="week", method='close')
dates = list(np.sort(holdings.Date.unique()))
dates.append(date.today()) # dates with transactions
rets = pd.Series() # weekly returns of total investments
vals = pd.Series() # total value of investments
perf = pd.Series() # performance, i.e. current value / invested value
max_value = 0
for d in range(len(dates) - 1):
tickers = holdings.Ticker[holdings.Date <= dates[d]]
from_date = dates[d] - np.timedelta64(7, 'D')
to_date = dates[d + 1]
shares = holdings.Shares[holdings.Date <= dates[d]]
price = weekly['Close'][tickers][from_date:to_date]
value = pd.Series(price.values.dot(shares.values), index=price.index)
rets = rets.append(get_returns(value))
price = data['Close'][tickers][dates[d]:dates[d + 1]]
value = pd.Series(price.values.dot(shares.values), index=price.index)
vals = vals.append(value)
buys = holdings.Price[holdings.Date <= dates[d]]
perf = perf.append(value / np.dot(buys.values, shares.values))
xmin, xmax = vals.index[0], vals.index[-1]
plt.figure(figsize=(15, 5))
plt.plot(rets.index, rets * 100, color='#1f77b4')
plt.hlines(y=0, xmin=xmin, xmax=xmax, linestyles='--', color='#1f77b4')
plt.ylabel("Weekly Return (%)", fontsize=20, color='#1f77b4')
plt.yticks(fontsize=14, color='#1f77b4')
plt.legend()
plt.twinx()
plt.plot(perf.index, (perf - 1) * 100, color='#ff7f0e')
plt.hlines(y=0, xmin=xmin, xmax=xmax, linestyles='--', color='#ff7f0e')
# plt.gca().set_ylim(bottom=0)
plt.ylabel("Total Return (%)", fontsize=20, color='#ff7f0e')
plt.yticks(fontsize=14, color='#ff7f0e')
plt.xlim(xmin, xmax)
from gui.tkinter_widget import display_dataframe, plot_embed_toolbar
import tkinter as tk
if frame is None:
root = tk.Tk()
else:
root = frame
plot_embed_toolbar(root, fig=plt.gcf()).grid(row=0, column=0, columnspan=2)
tk.Label(root,
text="""Notes: \n
'Annual Return' is just the rescaled weekly return.
'Performance' is the total return percentage of the day
(will be affected by buy/sell activities).\n
""").grid(row=1, column=0, padx=10, pady=10)
display_dataframe(root, summary).grid(row=1, column=1, padx=10, pady=10)
total_value = output['Value'].sum()
total_invest = (output['Buy Shares'] * output['Buy Price']).sum()
total_gain = total_value - total_invest
tk.Label(root, font='bold', text="total {:.2f} / {:.2f} = {:.2%}"\
.format(total_gain, total_invest, total_gain/total_invest))\
.grid(row=2, column=1, padx=10, pady=10)
if frame is None:
root.mainloop()