diff --git a/WHATSNEW.md b/WHATSNEW.md index 4e2c6ae9..ec5e9c51 100644 --- a/WHATSNEW.md +++ b/WHATSNEW.md @@ -183,7 +183,7 @@ your dependencies, specifically, `pandas>=0.18.0`, `seaborn>=0.6.0` and Wiecki](https://github.com/twiecki) ### Bug fixes -* Many depracation fixes for Pandas 0.18.0, seaborn 0.6.0, and zipline 0.8.4 +* Many deprecation fixes for Pandas 0.18.0, seaborn 0.6.0, and zipline 0.8.4 ## v0.4.0 (Dec 10, 2015) diff --git a/pyfolio/capacity.py b/pyfolio/capacity.py index 00864741..a16f93ef 100644 --- a/pyfolio/capacity.py +++ b/pyfolio/capacity.py @@ -208,12 +208,12 @@ def apply_slippage_penalty(returns, txn_daily, simulate_starting_capital, returns : pd.Series Time series of daily returns. txn_daily : pd.Series - Daily transaciton totals, closing price, and daily volume for + Daily transaction totals, closing price, and daily volume for each traded name. See price_volume_daily_txns for more details. simulate_starting_capital : integer capital at which we want to test backtest_starting_capital: capital base at which backtest was - origionally run. impact: See Zipline volumeshare slippage model + originally run. impact: See Zipline volumeshare slippage model impact : float Scales the size of the slippage penalty. diff --git a/pyfolio/plotting.py b/pyfolio/plotting.py index bd340136..ffe70e5d 100644 --- a/pyfolio/plotting.py +++ b/pyfolio/plotting.py @@ -72,7 +72,7 @@ def plotting_context(context='notebook', font_scale=1.5, rc=None): Config flags. By default, {'lines.linewidth': 1.5} is being used and will be added to any - rc passed in, unless explicitly overriden. + rc passed in, unless explicitly overridden. Returns ------- @@ -455,7 +455,7 @@ def plot_drawdown_periods(returns, top=10, ax=None, **kwargs): def plot_drawdown_underwater(returns, ax=None, **kwargs): """ - Plots how far underwaterr returns are over time, or plots current + Plots how far underwater returns are over time, or plots current drawdown vs. date. Parameters @@ -753,7 +753,7 @@ def plot_rolling_returns(returns, volatilities. Requires passing of benchmark_rets. cone_function : function, optional Function to use when generating forecast probability cone. - The function signiture must follow the form: + The function signature must follow the form: def cone(in_sample_returns (pd.Series), days_to_project_forward (int), cone_std= (float, or tuple), @@ -1408,7 +1408,7 @@ def plot_slippage_sweep(returns, positions, transactions, Prices and amounts of executed trades. One row per trade. - See full explanation in tears.create_full_tear_sheet. slippage_params: tuple - Slippage pameters to apply to the return time series (in + Slippage parameters to apply to the return time series (in basis points). ax : matplotlib.Axes, optional Axes upon which to plot. @@ -1877,7 +1877,7 @@ def plot_cones(name, bounds, oos_returns, num_samples=1000, ax=None, Account name to be used as figure title. bounds : pandas.core.frame.DataFrame Contains upper and lower cone boundaries. Column names are - strings corresponding to the number of standard devations + strings corresponding to the number of standard deviations above (positive) or below (negative) the projected mean cumulative returns. oos_returns : pandas.core.frame.DataFrame @@ -1890,7 +1890,7 @@ def plot_cones(name, bounds, oos_returns, num_samples=1000, ax=None, ax : matplotlib.Axes, optional Axes upon which to plot. cone_std : list of int/float - Number of standard devations to use in the boundaries of + Number of standard deviations to use in the boundaries of the cone. If multiple values are passed, cone bounds will be generated for each value. random_seed : int diff --git a/pyfolio/round_trips.py b/pyfolio/round_trips.py index 05c2eaad..44d44d75 100644 --- a/pyfolio/round_trips.py +++ b/pyfolio/round_trips.py @@ -193,7 +193,7 @@ def extract_round_trips(transactions, DataFrame with one row per round trip. The returns column contains returns in respect to the portfolio value while rt_returns are the returns in regards to the invested capital - into that partiulcar round-trip. + into that particular round-trip. """ transactions = _groupby_consecutive(transactions) diff --git a/pyfolio/tears.py b/pyfolio/tears.py index 53e5c6a5..64fa2d23 100644 --- a/pyfolio/tears.py +++ b/pyfolio/tears.py @@ -247,7 +247,7 @@ def create_simple_tear_sheet(returns, - Never accept market_data input (market_data = None) - Never accept sector_mappings input (sector_mappings = None) - Never perform bootstrap analysis (bootstrap = False) - - Never hide posistions on top 10 holdings plot (hide_positions = False) + - Never hide positions on top 10 holdings plot (hide_positions = False) - Always use default cone_std (cone_std = (1.0, 1.5, 2.0)) Parameters diff --git a/pyfolio/timeseries.py b/pyfolio/timeseries.py index 2b34d3b7..27a666fc 100644 --- a/pyfolio/timeseries.py +++ b/pyfolio/timeseries.py @@ -1120,7 +1120,7 @@ def summarize_paths(samples, cone_std=(1., 1.5, 2.), starting_value=1.): samples : numpy.ndarray Alternative paths, or series of possible outcomes. cone_std : list of int/float - Number of standard devations to use in the boundaries of + Number of standard deviations to use in the boundaries of the cone. If multiple values are passed, cone bounds will be generated for each value. @@ -1152,7 +1152,7 @@ def forecast_cone_bootstrap(is_returns, num_days, cone_std=(1., 1.5, 2.), """ Determines the upper and lower bounds of an n standard deviation cone of forecasted cumulative returns. Future cumulative mean and - standard devation are computed by repeatedly sampling from the + standard deviation are computed by repeatedly sampling from the in-sample daily returns (i.e. bootstrap). This cone is non-parametric, meaning it does not assume that returns are normally distributed. @@ -1164,7 +1164,7 @@ def forecast_cone_bootstrap(is_returns, num_days, cone_std=(1., 1.5, 2.), num_days : int Number of days to project the probability cone forward. cone_std : int, float, or list of int/float - Number of standard devations to use in the boundaries of + Number of standard deviations to use in the boundaries of the cone. If multiple values are passed, cone bounds will be generated for each value. starting_value : int or float @@ -1182,7 +1182,7 @@ def forecast_cone_bootstrap(is_returns, num_days, cone_std=(1., 1.5, 2.), ------- pd.DataFrame Contains upper and lower cone boundaries. Column names are - strings corresponding to the number of standard devations + strings corresponding to the number of standard deviations above (positive) or below (negative) the projected mean cumulative returns. """ diff --git a/pyfolio/txn.py b/pyfolio/txn.py index 9fb8db10..aa29bd14 100644 --- a/pyfolio/txn.py +++ b/pyfolio/txn.py @@ -60,7 +60,7 @@ def make_transaction_frame(transactions): Returns ------- df : pd.DataFrame - Daily transaction volume and dollar ammount. + Daily transaction volume and dollar amount. - See full explanation in tears.create_full_tear_sheet. """