tsignals

API documentation for pandas_ta.trend.tsignals Python function.

tsignals(trend, asbool=None, trend_reset=0, trade_offset=None, drift=None, offset=None, **kwargs)

Trend Signals

Given a Trend, Trend Signals returns the Trend, Trades, Entries and Exits as boolean integers. When ‘asbool=True’, it returns Trends, Entries and Exits as boolean values which is helpful when combined with the vectorbt backtesting package.

A Trend can be a simple as: ‘close’ > ‘moving average’ or something more complex whose values are boolean or integers (0 or 1).

Examples: ta.tsignals(close > ta.sma(close, 50), asbool=False) ta.tsignals(ta.ema(close, 8) > ta.ema(close, 21), asbool=True)

Source: Kevin Johnson

Calculation:

Default Inputs:

asbool=False, trend_reset=0, trade_offset=0, drift=1

trades = trends.diff().shift(trade_offset).fillna(0).astype(int) entries = (trades > 0).astype(int) exits = (trades < 0).abs().astype(int)

Args:

trend (pd.Series): Series of ‘trend’s. The trend can be either a boolean or

integer series of ‘0’s and ‘1’s

asbool (bool): If True, it converts the Trends, Entries and Exits columns to

booleans. When boolean, it is also useful for backtesting with vectorbt’s Portfolio.from_signal(close, entries, exits) Default: False

trend_reset (value): Value used to identify if a trend has ended. Default: 0 trade_offset (value): Value used shift the trade entries/exits Use 1 for

backtesting and 0 for live. Default: 0

drift (int): The difference period. Default: 1 offset (int): How many periods to offset the result. Default: 0

Kwargs:

fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method

Returns:

pd.DataFrame with columns: Trends (trend: 1, no trend: 0), Trades (Enter: 1, Exit: -1, Otherwise: 0), Entries (entry: 1, nothing: 0), Exits (exit: 1, nothing: 0)