'''
R-Squared coefficient of determination
R**2 = 1 - SSres/SStot
'''
def r_squared(series: pd.Series, predicted_values: pd.Series)->np.float64:
ss_res_series = ss_res(series, predicted_values)
ss_tot_series = ss_tot(series)
r_squared = 1 - (ss_res_series/ss_tot_series)
return r_squared
if __name__ == '__main__':
# load data
ticker = 'HECP'
series = load_sample_data(ticker)
close_price = series.iloc[-30:, 1]
print('Symbol: ', ticker)
print('naive prediction--')
predicted_values = series.iloc[-30:, 1].shift(1)
r2 = r_squared(close_price, predicted_values).round(3)
print('R squared: ', r2)
exp_range = ExponentialRange(4, 8, 1/4)
test_columns = ['date', 'open', 'close', 'low', 'high', 'volume']
test_df = pd.DataFrame(
np.random.rand(exp_range.max, 6),
columns=test_columns,
index=pd.date_range('01-01-1900', periods=exp_range.max, freq=pd.Timedelta(seconds=10))
)
axs[0].grid(True)
axs[1].set_title(ticker+' RSI')
axs[1].xaxis.set_major_locator(locator)
axs[1].xaxis.set_major_formatter(formatter)
axs[1].set_ylim(0, 100)
axs[1].plot(dataframe[['rsi']], c='k', lw=1.5, ls='dotted', label="RSI")
axs[1].set_xlabel('Date')
axs[1].set_ylabel('rsi')
axs[1].legend(loc='lower right')
axs[1].grid(True)
plt.show()
if __name__ == '__main__':
ticker = 'HRVC'
dataframe = load_sample_data(ticker)
window = 30
rsi = compute_rsi(dataframe, window)
to_plot = dataframe.copy()
compute_bb(to_plot)
x = -window # define the date range for plot to plot
to_plot = to_plot.iloc[x:]
to_plot['rsi'] = rsi[x:]
_plot(ticker, to_plot)
exit
# Indicators module contains core qttk functionality
from qttk.indicators import compute_rsi
# qttk is 'batteries included' with simulated data
from qttk.utils.sample_data import load_sample_data
# Load a ticker from csv into dataframe
df = load_sample_data('AWU')
print(df.head())
rsi = compute_rsi(df)
print(rsi[-10:])
# Multiply sign by the boolean. This will cast the boolean to an integer
# either 1 or 0 and then multiply by the sign either (-1, 0, 1).
return macd_sign * (macd_sign != macd_shifted_sign)
if __name__ == '__main__':
required_ohlcv_columns = pd.Series(
['open', 'high', 'low', 'close', 'volume'])
# removed date as a required column because it is set as the dataframe index
# when the csv is read
# required_ohlcv_columns = pd.Series(['date', 'open', 'high', 'low', 'close', 'volume'])
data = 'AWU.csv' # name of data file to use
demo_macd(required_columns=required_ohlcv_columns)
# optional loop
# script_dir = os.path.dirname(__file__)
# csv_files = os.path.join(script_dir, 'data', 'eod', '*.csv')
# for csv_file in glob.glob(csv_files):
# main(csv_file)
# try signal
from qttk.utils.sample_data import load_sample_data
csv_file = load_sample_data('AWU')
macd = compute_macd(csv_file)
macd_signal = create_macd_signal(macd['MACD'])
macd_signal = macd_signal.iloc[-180:]
macd_signal.plot()
plt.show()
right=0.989,
hspace=0.918,
wspace=0.2)
axs[0].set_title = 'Close Prices: {ticker}'.format(ticker=ticker)
axs[0].plot(close_price.loc[close_price.index > cutoff])
axs[1].set_title = f'Chaikin Money Flow: {ticker}'
axs[1].plot(cmf.loc[chaikin_money_flow.index > cutoff])
axs[2].set_title = f'Signal: {ticker}'
axs[2].plot(cmf_signal.loc[signal.index > cutoff])
return fig, axs
if __name__ == '__main__':
ticker = 'AWU'
demo_plot = True
if sys.argv[1:]:
demo_plot = False
demo_df = load_sample_data(ticker)
cmf = calculate_chaikin_money_flow(demo_df)
cmf_signal = calculate_chaikin_money_flow_signal(cmf)
if demo_plot:
fig, axs = _format_chaikin_plot(demo_df.close, cmf, cmf_signal, ticker)
plt.show(block=False)