iCHSSKJr SSKJr SSKJrJr SSKJrJ r J r SSK J r SSK JrJrJrJrJrJr /SQrS*S \S \S \S \ S \4 SjjrS*S \S\ S \S \ S \4 SjjrS*S \S \S \S \ S \4 SjjrS*S \S\ S \S \ S \4 SjjrS+S \S \S\S\S \S \ S\S \4SjjrS+S \S\ S\S\S \S \ S \4SjjrS,S\S\ S\ S\S\S\S\S\S \ S \4S jjrS-S \S \S\S \S \ S \4 S!jjrS.S"\S#\S$\ S%\ S \ S\S \4S&jjrS/S'\S\ \-S\ \-S\S(\S#\S$\ S \ S\S \4S)jjrg)0)partial)nan) DataFrameSeries)DictLikeIntIntFloat)zero)v_boolv_driftv_floatv_intv_offsetv_series) above above_valuebelow below_valuecross cross_valuesignalstsignalsxsignalsNxyasintoffsetreturnc 0[[SS9"X4X#S.UD6$)aAbove Determines if each ```x``` value is above (or ```>=```) each ```y``` value. Parameters: x (Series): ```x``` y (Series): ```y``` asint (bool): Returns as ```Int```. offset (Int): Post shift. Default: ```0``` Returns: (Series): State where ```x >= y```. Example: ```py x = Series([4, 2, 0, -1, 1]) y = Series([1, 1, 1, 1, 1]) x_above_y = ta.above(x, y) # x_above_y = Series([1, 1, 0, 0, 1]) ``` Trrrr _above_belowrrrrkwargss S/var/www/html/trading/venv/lib/python3.13/site-packages/pandas_ta/utils/_signals.pyrr!s!2 =```) a constant ```value```. Parameters: x (Series): ```x``` value (IntFloat): Value to compare with ```x```. asint (bool): Returns as ```Int```. Returns: (Series): State where ```x >= y```. Example: ```py x = Series([4, 2, 0, -1, 1]) x_above_1 = ta.above_value(x, 1) # x_above_1 = Series([1, 1, 0, 0, 1]) ``` [X] value is not a numberN._indexnameTr r! isinstanceintfloatprintrr.replacerr#rr(rrr%rs r&rr=s]0 ec5\ * * )*uGGUG*<* B ~*R#u66 "9VLH eFKF (x}} $v{{O"9@H (x}}  ~*R#u66 "9VLH eFKF (x}} $v{{O"9@H (x}}   )!4!4  DHC V) rrr?r rBr2r@r/rC)rrrrrr%rDs r&r#r#bs*  A A f FGGDMGGDM && ..%{--'ffXQes5Qqvvh?GLG Nr'trendasbool trade_offsetdriftc [U5nUcg[US5n[US5n[U5n[ U5nUR [ 5nURU5RU5RS5R [ 5nUS:R [ 5nUS:R5R [ 5n U(a?UR [5nUR [5nU R [5n SUSUSUSU 0n [XRS9n US:waU RU5n S U;aU RUS S S 9 S U lS U lU $)aTrend Signals This function creates Trend, Trades, Entries and Exit values per bar when given a trend condition e.g. ```trend = close > sma(close, 50)```. Source: * Kevin Johnson Parameters: trend (Series): ```trend``` Series. Boolean or integer values of ```0``` and ```1``` asbool (bool): Return booleans. Default: ```False``` trade_offset (value): Shift trade entries/exits with live: ```0``` and backesting: ```1```. Default: ```0``` drift (int): Difference amount. Default: ```1``` offset (int): Post shift. Default: ```0``` Other Parameters: fillna (value): ```pd.DataFrame.fillna(value)``` Returns: (DataFrame): 4 columns Note: Column Detail * Trends (trend: 1, no trend: 0) * Trades (Enter: 1, Exit: -1, Otherwise: 0) * Entries (entry: 1, nothing: 0) * Exits (exit: 1, nothing: 0) Note: Details A ```trend``` is a state or condition, that is as simple as ```Close > MA``` or something more complex that has boolean or integer (trend: 1, no trend: 0) values. Tip: VectorBT * For backtesting, set ```trade_offset=1```. * Setting ```asbool=True``` is useful for backtesting with vectorbt's ```Portfolio.from_signal(close, entries, exits)``` method. Example: These are two different outcomes for each (long/short) position and depends on the source and it's behavior. Signals when ```Close > SMA50(Close)``` ta.tsignals(close > ta.sma(close, 50), asbool=False) Signals when ```EMA(Close, 8) > EMA(Close, 21)``` ta.tsignals(ta.ema(close, 8) > ta.ema(close, 21), asbool=True) Warning: Check ALL outcomes BEFORE making an Issue NFr TS_Trends TS_Trades TS_EntriesTS_Exits)r.fillnaTinplaceTSr\)rr rr rrBr2diffr@reabsboolrr.r/rC) r\r]r^r_rr%trendstradesentriesexitsdatarPs r&rrsVx UOE } FE "Fq)L ENE f F\\# F [[  % %l 3 : :1 = D DS IFz!!#&G aZ    % %c *E t$..& T" f f w U  D 4|| ,B{ XXf 6 &"D 1BGBK Ir'sourcelongc [U5nUcg[U5nU(a[X5n [XSS9*n O[XSS9n [X5*n X-n U RS[0SS9 U R SSS9 U R SSS9 U S:R[5n U(dS U - n UUUS .n [U 40U D6n[S URS S UR- 05 S U;aUR US SS9 SUl SUl U$)aCross Signals This function creates Trend, Trades, Entries and Exits values per bar for crossing events. Sources: * Kevin Johnson Parameters: source (Series): ```source``` Signal xa (Series): Series the Signal crosses above if ```above=True``` xb (Series): Series the Signal crosses below if ```above=True``` above (bool): The ```source``` crossing; below is ```False```. Default: ```True``` long (bool): The ```source``` position; short is ```False```. Default: ```True``` offset (int): Post shift. Default: ```0``` asbool (bool): Return booleans. Default: ```False``` trade_offset (value): Shift trade entries/exits with live: ```0``` and backesting: ```1```. Default: ```0``` Other Parameters: fillna (value): ```pd.DataFrame.fillna(value)``` Returns: (DataFrame): 4 columns Note: Column Detail * Trends (trend: 1, no trend: 0) * Trades (Enter: 1, Exit: -1, Otherwise: 0) * Entries (entry: 1, nothing: 0) * Exits (exit: 1, nothing: 0) Tip: VectorBT * For backtesting, set ```trade_offset=1```. * Setting ```asbool=True``` is useful for backtesting with vectorbt's ```Portfolio.from_signal(close, entries, exits)``` method. Example: These are two different outcomes for each (long/short) position and depends on the source and it's behavior. rsi = df.ta.rsi() When RSI crosses above 20 and then below 80 in a long position: ta.xsignals(source=rsi, xa=20, xb=80, above=True, long=True) # Simpler # ta.xsignals(rsi, 20, 80, True, True) When RSI crosses below 20 and then above 80 in a long position: ta.xsignals(source=rsi, xa=20, xb=80, above=False, long=True) # Simpler # ta.xsignals(rsi, 20, 80, False, True) * Similarly, short positions (```long=False```) also differ depending on ```above``` state. Warning: Check ALL parameter combination outcomes BEFORE making an Issue. 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