Using Python's `help()` at the REPL¶

You don't need to leave your terminal to discover Fundcloud's API. Every public class, function, and accessor method ships a NumPy-style docstring with Parameters, Returns, and Examples blocks — so the Python introspection tools you already know light up.

The one-liner¶

from fundcloud.metrics import metrics
help(metrics)

You get the full docstring: what metrics(...) computes, every parameter with its type, what the return value contains, and a runnable example.

Deep-diving a method¶

from fundcloud.sim import Simulator
help(Simulator.run_strategy)
help(Simulator.run_weights)
help(Simulator.run_signals)
help(Simulator.run_orders)

Same for the .fc accessor methods:

import pandas as pd
import fundcloud  # registers the .fc accessor

help(pd.DataFrame.fc.run_dca)     # preset wrapper for DCA
help(pd.DataFrame.fc.render_pdf)  # tear-sheet PDF
help(pd.Series.fc.metrics)        # ~55-metric bundle

Jupyter shortcut¶

In a notebook, a single ? gives you the docstring; ?? shows the source:

returns.fc.metrics?
fundcloud.metrics.probabilistic_sharpe??

Discovering what's available¶

Tab-completion at the REPL¶

import fundcloud.data as fcd
dir(fcd)
# → ['AV', 'Backend', 'BaseBackend', 'Binance', 'CSV', 'Catalog',
#    'DatasetSpec', 'DuckDB', 'FMP', 'Memory', 'Parquet',
#    'ReadOnlyError', 'WriteMode', 'YF', ...]

The network-backed backends (YF, FMP, AV, Binance) are lazy-imported, but __dir__ surfaces them anyway — so tab-completion "just works" in IPython, Jupyter, and modern editors.

The same pattern applies to fundcloud.sim, fundcloud.optimize, fundcloud.validate, and — most striking — fundcloud.features.indicators, where all 158 auto-wrapped TA-Lib indicators are tab-completable:

from fundcloud.features import indicators
len([x for x in dir(indicators) if x.isupper()])
# → 158

Listing every TA-Lib indicator¶

from fundcloud.features.indicators import list_indicators, GROUPS

list_indicators()[:5]
# → ['ACOS', 'AD', 'ADD', 'ADOSC', 'ADX']

GROUPS["Momentum Indicators"][:3]
# → ['ADX', 'ADXR', 'APO']

The full one-shot metrics bundle¶

import pandas as pd
import fundcloud                                          # .fc accessor
returns = pd.read_csv("my_returns.csv", index_col="date",
                      parse_dates=True)["return"]
m = returns.fc.metrics()                                  # pd.Series of ~55 metrics
m.head()

help(returns.fc.metrics) shows what each row means.

Where to go from `help()`¶

Metrics catalogue — Portfolio metrics.
Simulator entry points — Simulator.
Accessor index — each .fc.* method links back to its free function in API reference.

The convention: if a method shows up in dir(fundcloud.something), it has a docstring worth reading.

Using Python's help() at the REPL¶