Financial Series#

Financial Series is an abstract class representing financial time series.

Concrete implementations include Prices, Returns, CumulativeReturns

class portfolio_plan.financialseries.Prices(data: DataFrame, name: str)#

Bases: FinancialSeries

Parameters:

data (pd.DataFrame) – The data including financial prices.
name (str) – The name of this set of financial prices.

Note

It is recommended to derive Prices instance from a resource rather than from direct instantiation. A resource, can be a file, an in memory parquet buffer, a network resource. Refer to Resource

plot(scale_x: bool = True)#: Plot all prices candle chart

plot_close(scale_x: bool = True) → ggplot#: Plot all price close price

plot_high(scale_x: bool = True) → ggplot#: Plot all prices high price

plot_low(scale_x: bool = True) → ggplot#

Plot all prices low price

Example

>>> from portfolio_plan.financialseries import Prices, Frequency
>>> from portfolio_plan.resource import File
>>> from portfolio_plan.data import example_prices_path

>>> resource = File(path=example_prices_path(), frequency=Frequency.BDAILY)
>>> prices = resource.fetch(
...     ["A", "B"],
...     start_date="2025-01-01",
... end_date="2025-01-31"
)
>>> price.plot_low()

plot_open(scale_x: bool = True) → ggplot#: Plot all prices open price

class portfolio_plan.financialseries.Returns(data: DataFrame, name: str)#

Bases: FinancialSeries

Parameters:: data (pd.DataFrame) – A dataframe containing the OHLC returns

avg(weights: Weights, portfolio_name: str = 'Portfolio') → Returns#: Compute the average return by date

cumulative(name: str, starting_value: float | List[float] = 1.0, keepna: bool = True, check_base: bool = True, periods: List[Period] | None = None) → CumulativeReturns#

Parameters:

name (str) – See portfolio_plan.financialseries.CumulativeReturns()
starting_value (int | List[int]) – See empyrical.cum_returns() If starting_value is a list, it must be of length 4. Each element is mapped (in order) to: - open - close - high - low

Warning

I recommend setting keepna to True. When False, inconsistencies between the frequencies of the scale of the plotted dates and the actual frequency of the data could lead to misinterpretation.

plot(scale_x: bool = True)#: Plot all returns candle chart

plot_close(scale_x: bool = True) → ggplot#: Plot all returns close price

plot_high(scale_x: bool = True) → ggplot#: Plot all returns high price

plot_low(scale_x: bool = True) → ggplot#

Plot all returns low price

Example

>>> from portfolio_plan.financialseries import Returns, Frequency
>>> from portfolio_plan.resource import File
>>> from portfolio_plan.data import example_prices_path
>>> from portfolio_plan.utils import returns_from_prices

>>> resource = File(path=example_prices_path(), frequency=Frequency.BDAILY)
>>> prices = resource.fetch(
...     ["A", "B"],
...     start_date="2025-01-01",
...     end_date="2025-01-31"
... )
>>> returns = Returns(returns_from_prices(prices.data))
>>> returns.plot_low()

>>> returns.cumulative().plot_low()

plot_open(scale_x: bool = True) → ggplot#: Plot all returns open price

from portfolio_plan import Frequency
from portfolio_plan.resource import File
from portfolio_plan.utils import returns_from_prices
from portfolio_plan.data import example_prices_path
from datetime import datetime


START_DATE = datetime.strptime("2025-01-30", "%Y-%m-%d")
END_DATE = datetime.strptime("2025-03-01", "%Y-%m-%d")


prices = File(path=example_prices_path(), frequency=Frequency.BDAILY).fetch(
    ["A"],
    start_date=START_DATE,
    end_date=END_DATE,
    name="Prices"
)

returns_from_prices(prices.data)

	Returns	open	close	high	low
symbol	date
A	2025-01-30	NaN	NaN	NaN	NaN
	2025-01-31	0.000000	0.000000	0.000000	0.000000
	2025-02-03	0.000918	0.000471	0.000239	0.000317
	2025-02-04	0.001116	0.000573	0.000290	0.000386
	2025-02-05	0.000000	0.000000	0.000000	0.000000
	2025-02-06	-0.000054	-0.000028	-0.000014	-0.000019
	2025-02-07	-0.001246	-0.000640	-0.000324	-0.000431
	2025-02-10	0.000000	0.000000	0.000000	0.000000
	2025-02-11	0.002883	0.001480	0.000750	0.000996
	2025-02-12	-0.000906	-0.000466	-0.000236	-0.000313
	2025-02-13	0.000000	0.000000	0.000000	0.000000
	2025-02-14	0.002698	0.001386	0.000703	0.000933
	2025-02-17	-0.000675	-0.000347	-0.000176	-0.000234
	2025-02-18	0.000000	0.000000	0.000000	0.000000
	2025-02-19	0.001734	0.000892	0.000452	0.000600
	2025-02-20	0.001595	0.000821	0.000417	0.000553
	2025-02-21	0.000000	0.000000	0.000000	0.000000
	2025-02-24	-0.001556	-0.000801	-0.000407	-0.000540
	2025-02-25	0.000529	0.000273	0.000138	0.000184
	2025-02-26	0.001125	0.000579	0.000294	0.000390
	2025-02-27	0.000000	0.000000	0.000000	0.000000
	2025-02-28	0.001043	0.000537	0.000273	0.000362

class portfolio_plan.financialseries.CumulativeReturns(data: DataFrame, name: str, check_base: bool = True, periods: List[Period] | None = None, check_frequency: bool = True)#

Bases: FinancialSeries

Parameters:

data (pd.DataFrame) – A dataframe containing cumulative OHLC returns
name (str) – Name of the cumulative returns
check_base (bool) – Whether to enforce first value to be equal across open, close, high, low. Defaults to True
periods (List[Period] | None) – See portfolio_plan.utils.Period()

plot(scale_x: bool = True)#: Plot all cumulative returns candle chart

plot_close(scale_x: bool = True) → ggplot#: Plot all cumulative returns close price

plot_high(scale_x: bool = True) → ggplot#: Plot all cumulative returns high price

plot_low(scale_x: bool = True) → ggplot#: Plot all cumulative returns high price

plot_open(scale_x: bool = True) → ggplot#: Plot all cumulative returns open price

Financial Series#

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