def get_interest_rate(self, date):
#print(date)
#print("Date type "+str(type(date.to_pydatetime())))
#print(self.interest_rate_list[0].maturity)
if hasattr(self, "interpolation"):
return self.interpolation(utils.toTimestamp(date.to_pydatetime()))
else:
return 0
def candle(self, ticker, field='Adj Close'):
week_formatter = DateFormatter('%b %d')
mondays = WeekdayLocator(MONDAY)
data = self.DataReader(ticker)
z = data.reset_index()
fig, ax = plt.subplots()
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_major_formatter(week_formatter)
z['date_num']=z['Date'].apply(lambda date: mdates.date2num(date.to_pydatetime()))
subset = [tuple(x) for x in z[['date_num', 'Open', 'High', 'Low', 'Close']].values]
candlestick_ohlc(ax, subset, width=0.6, colorup='g', colordown='r')
def candle(self, ticker, field='Adj Close'):
week_formatter = DateFormatter('%b %d')
mondays = WeekdayLocator(MONDAY)
data = self.DataReader(ticker)
z = data.reset_index()
fig, ax = plt.subplots()
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_major_formatter(week_formatter)
z['date_num'] = z['Date'].apply(
lambda date: mdates.date2num(date.to_pydatetime()))
subset = [
tuple(x)
for x in z[['date_num', 'Open', 'High', 'Low', 'Close']].values
]
candlestick_ohlc(ax, subset, width=0.6, colorup='g', colordown='r')
def parse_ibkr_report_tradelog(path: Path) -> TradesSet:
header_prefix = "Trades,Header,"
data_prefix = "Trades,Data,Order,Stocks,"
with path.open("r") as f:
lines = f.readlines()
columns = None
data_lines = []
for line in lines:
line = line.strip()
if line.startswith(header_prefix) and columns is None:
# skip Order,Stocks columns on top of the prefix cols
columns = line[len(header_prefix):].split(",")[2:]
elif line.startswith(data_prefix):
data_lines.append(line[len(data_prefix):])
out: TradesSet = defaultdict(set)
if not data_lines:
return out
df = pd.read_csv(StringIO("\n".join(data_lines)),
thousands=",",
header=None)
df.columns = columns
df = df[["Date/Time", "Symbol", "Quantity", "T. Price", "Comm/Fee"]]
# the account management reports use eastern time natively
# noinspection PyUnresolvedReferences
df["Date/Time"] = pd.to_datetime(
df["Date/Time"]).dt.tz_localize(TZ_EASTERN)
for date, symbol, qty, price, comm in df.itertuples(index=False):
if qty == 0:
continue
t = date.to_pydatetime()
# if qty < 0, we lower price; else we increase.
price -= comm / qty
out[symbol].add(Trade(symbol, t, qty, price))
return out
def download_single_day(date, path, overwrite):
formatted_date = date.to_pydatetime().strftime('%m-%d-%Y')
destination_file = path.joinpath(f'{formatted_date}.csv')
if destination_file.exists() and not overwrite:
logger.info(f'{formatted_date} -- already exists')
return
url = DATA_URL + f'{formatted_date}.csv'
logger.info(f'{formatted_date} -- downloading file: {url}')
response = requests.get(url)
assert meets_expectations(response)
with open(destination_file, 'w') as fh:
fh.write(response.text)
logger.info(f'{formatted_date} -- saved to {destination_file}')
def grab_historical_prices(self,
start: datetime,
end: datetime,
bar: str = '1d',
symbols: Optional[List[str]] = None) -> dict:
self._bar = bar
new_prices = []
if not symbols:
symbols = self.portfolio.positions.keys()
for symbol in symbols:
historical_price_response = yf.download(
symbol,
start=start.strftime("%Y-%m-%d"),
end=end.strftime("%Y-%m-%d"),
group_by="ticker",
interval=bar)
historical_price_response_dict = historical_price_response.to_dict(
orient='index')
for date in historical_price_response_dict:
new_price_mini_dict = {}
new_price_mini_dict['symbol'] = symbol
new_price_mini_dict['open'] = historical_price_response_dict[
date]['Open']
new_price_mini_dict['close'] = historical_price_response_dict[
date]['Close']
new_price_mini_dict['low'] = historical_price_response_dict[
date]['Low']
new_price_mini_dict['high'] = historical_price_response_dict[
date]['High']
new_price_mini_dict['volume'] = historical_price_response_dict[
date]['Volume']
new_price_mini_dict['datetime'] = date.to_pydatetime()
new_prices.append(new_price_mini_dict)
self.historical_prices['aggregated'] = new_prices
return self.historical_prices
#creates a copy of the dataMU for simpler graphing
dataMU2 = dataMU[1:100]
#creates a copy of myMU data
subset = myMU.copy()
#creates a date column setting to date from another DF instance
subset['Date'] = myMU.index
#moves the data index into its own column
subset.reset_index(inplace=True)
#drops the index named column
subset.drop(['index'], axis=1, inplace=True)
#creates new column, dateNum, as a number from date
subset['dateNum'] = pd.to_datetime(subset['Date'])
#creates a number from the date
subset['dateNum'] = subset['dateNum'].apply(
lambda date: mdates.date2num(date.to_pydatetime()))
subsetAsTuples = [
tuple(x) for x in subset[
['dateNum', '1. open', '2. high', '3. low', '4. close']].values
]
#graph based on months
from matplotlib.dates import DateFormatter
#monthFormatter = DateFormatter('%b %d')
import matplotlib.dates as mdates
yearFormatter = DateFormatter('%b %Y')
#months = mdates.MonthLocator()
years = mdates.YearLocator()