# -*- coding: utf-8 -*-
from pandas import DataFrame
from pandas_ta.utils import get_drift, get_offset, verify_series
[docs]def tsignals(trend, asbool=None, trend_reset=0, trade_offset=None, drift=None, offset=None, **kwargs):
"""Indicator: Trend Signals"""
# Validate Arguments
trend = verify_series(trend)
asbool = bool(asbool) if isinstance(asbool, bool) else False
trend_reset = int(trend_reset) if trend_reset and isinstance(trend_reset, int) else 0
if trade_offset !=0:
trade_offset = int(trade_offset) if trade_offset and isinstance(trade_offset, int) else 0
drift = get_drift(drift)
offset = get_offset(offset)
# Calculate Result
trends = trend.astype(int)
trades = trends.diff(drift).shift(trade_offset).fillna(0).astype(int)
entries = (trades > 0).astype(int)
exits = (trades < 0).abs().astype(int)
if asbool:
trends = trends.astype(bool)
entries = entries.astype(bool)
exits = exits.astype(bool)
data = {
f"TS_Trends": trends,
f"TS_Trades": trades,
f"TS_Entries": entries,
f"TS_Exits": exits,
}
df = DataFrame(data, index=trends.index)
# Offset
if offset != 0:
df = df.shift(offset)
# Handle fills
if "fillna" in kwargs:
df.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
df.fillna(method=kwargs["fill_method"], inplace=True)
# Name & Category
df.name = f"TS"
df.category = "trend"
return df
tsignals.__doc__ = \
"""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)
"""