def cumulative_state(transactions):
dt = pd.DataFrame(transactions)
dates = get_dates(transactions)
timestamps = get_timestamps(dates)
tickers = rt.all_tickers(transactions)
cs = prepare_cs(dates, timestamps)
for ticker in tickers:
lq = list()
lp = list()
lv = list()
for ldate in dates:
quantity = dt[(dt.ticker == ticker) & (dt.dt_date <= ldate)]['quantity'].sum()
price = cv.close_price_from(ldate, ticker)
lq.append(quantity)
lp.append(price)
lv.append(quantity * price)
cs[ticker + '_q'] = lq
cs[ticker + '_p'] = lp
cs[ticker + '_v'] = lv
tv_list = list()
for ldate in dates:
total_value = 0.0
for ticker in tickers:
total_value = total_value + float(cs[cs.dt_date == ldate][ticker + '_v'])
tv_list.append(total_value)
cs['total_value'] = tv_list
return cs
def worth_at_date(transaction_file, date):
df = read_file(transaction_file)
qs = df[df.dt_date <= date].groupby(['owner', 'ticker', 'quoted_in']).quantity.sum()
return pd.DataFrame(qs).apply(lambda x: cv.close_price_from(date, x.name[1]) * x.quantity * cv.getUSDPair(date, x.name[2]),
axis=1).reset_index().groupby('owner').sum()
def value_at(date, transactions):
df = pd.DataFrame(transactions)
df = df[df.dt_date <= date].groupby(['owner', 'ticker']).quantity.sum()
df = pd.DataFrame(df).apply(lambda x: cv.close_price_from(date, x.name[1]) * x.quantity,
axis=1).reset_index().groupby('owner').sum()
return df
