import pandas as pd
from pypm import data_io, portfolio
symbol = 'AWU'
df = data_io.load_eod_data(symbol)
shares_to_buy = 50
for i, row in enumerate(df.itertuples()):
date = row.Index
price = row.close
if i == 123:
position = portfolio.Position(symbol, date, price, shares_to_buy)
elif 123 < i < 234:
position.record_price_update(date, price)
elif i == 234:
position.exit(date, price)
position.print_position_summary()
# Returns ...
# AWU Trade summary
# Date: Wed Jun 30, 2010 -> Tue Dec 07, 2010 [111 days]
# Price: $220.34 -> $305.98 [38.9%]
# Value: $11017.0 -> $15299.0 [$4282.0]
def calculate_money_flow_volume(df: pd.DataFrame, n: int = 20) -> pd.Series:
"""
Calculates money flow volume, or q_t in our formula
"""
return calculate_money_flow_volume_series(df).rolling(n).sum()
def calculate_chaikin_money_flow(df: pd.DataFrame, n: int = 20) -> pd.Series:
"""
Calculates the Chaikin money flow
"""
return calculate_money_flow_volume(df, n) / df['volume'].rolling(n).sum()
if __name__ == '__main__':
data = load_eod_data('AWU')
closes = data['close']
sma = calculate_simple_moving_average(closes, 10)
macd = calculate_macd_oscillator(closes, 5, 50)
bollinger_bands = calculate_bollinger_bands(closes, 100)
bollinger_bands = bollinger_bands.assign(closes=closes)
bollinger_bands.plot()
cmf = calculate_chaikin_money_flow(data)
# cmf.plot()
import matplotlib.pyplot as plt
plt.show()
from pypm import data_io, metrics
df = data_io.load_eod_data('AWU')
print(metrics.calculate_cagr(df['close']))