README
¶
BanTA Technical Analysis Library
中文文档
banta is a high-performance technical analysis indicator library that supports both state-caching and parallel computation modes. It aims to provide a highly flexible, high-performance, and user-friendly indicator framework.
- State-Caching Mode: Updates and caches on each candle, eliminating the need to recalculate historical data. Indicator results are globally reused, similar to TradingView.
- Parallel Computation Mode: Computes all candles at once without caching. New candles require a full recalculation, similar to TA-Lib.
- NaN Compatibility: Intelligently skips NaN values in input data, resuming calculations with the previous state.
- Rigorous Testing: Each indicator is validated with unit tests under various conditions and compared against results from common indicator libraries.
- Lightweight & Dependency-Free: Pure Go implementation with zero external dependencies.
- Python Support: Packaged as the
bbtamodule via gopy, ready to be imported and used directly in Python.
Supported Indicators
Consistency Comparison with Common Indicator Platforms
| banta | MyTT | TA-lib Class | TA-lib Metastock | Pandas-TA | TradingView |
|---|---|---|---|---|---|
| AvgPrice | ✔ | ✔ | ✔ | ✔ | ✔ |
| Sum | ✔ | ✔ | ✔ | ✔ | ✔ |
| SMA | T1 | ✔ | ✔ | ✔ | ✔ |
| EMA | T1 | T1 | ✔ | ✔ | T2 |
| EMABy1 | ✔ | T1 | T2 | T2 | T3 |
| RMA | -- | -- | -- | T1 | -- |
| VWMA | -- | -- | -- | ✔ | ✔ |
| WMA | ✔ | ✔ | ✔ | ✔ | ✔ |
| HMA | -- | -- | -- | ✔ | ✔ |
| TR | -- | ✔ | ✔ | ✔ | -- |
| ATR | T1 | ✔ | ✔ | T2 | T3 |
| MACD | T1 | T2 | T1 | ✔ | T3 |
| RSI | T1 | ✔ | ✔ | T2 | T3 |
| KDJ | T1 | T2 | T1 | T3 | ✔ |
| Stoch | -- | ✔ | ✔ | -- | ✔ |
| BBANDS | ✔ | ✔ | ✔ | ✔ | ✔ |
| Aroon | -- | ✔ | ✔ | ✔ | T1 |
| ADX | -- | ✔ | ✔ | T1 | T2 |
| ADXBy1 | -- | T1 | T1 | T2 | ✔ |
| PluMinDI | -- | ✔ | ✔ | -- | -- |
| PluMinDM | -- | ✔ | ✔ | -- | -- |
| ROC | ✔ | ✔ | ✔ | -- | ✔ |
| TNR/ER | -- | -- | -- | -- | -- |
| CCI | ✔ | ✔ | ✔ | ✔ | ✔ |
| CMF | -- | -- | -- | ✔ | ✔ |
| KAMA | -- | ✔ | ✔ | T1 | ✔~ |
| WillR | -- | ✔ | ✔ | ✔ | ✔ |
| StochRSI | -- | ✔ | ✔ | ✔ | ✔~ |
| MFI | ✔ | ✔ | ✔ | ✔ | ✔ |
| RMI | -- | -- | -- | -- | ✔~ |
| CTI | -- | -- | -- | ✔ | T1 |
| LinReg | -- | -- | -- | ✔ | ? |
| CMO | -- | ✔ | ✔ | ✔ | T1 |
| CMOBy1 | -- | T1 | T1 | T1 | ✔ |
| CHOP | -- | -- | -- | ✔ | T1 |
| ALMA | -- | -- | -- | ✔ | T1 |
| Stiffness | -- | -- | -- | -- | ✔ |
| PercentRank | -- | -- | -- | -- | ✔~ |
| CRSI | -- | -- | -- | -- | ✔~ |
| CRSIBy1 | -- | community | -- | -- | -- |
| DV | -- | -- | -- | -- | -- |
| UTBot | -- | -- | -- | -- | ✔ |
| STC | -- | -- | -- | -- | ✔ |
-- This platform does not have the indicator
✔ Consistent with this platform's results
✔~ Mostly consistent with this platform's results (minor deviations)
Ti Inconsistent with this platform's results
How to Use (State-Caching Mode)
import (
"fmt"
ta "github.com/banbox/banta"
)
var envMap = make(map[string]*ta.BarEnv)
func OnBar(symbol string, timeframe string, bar *ta.Kline) {
envKey := fmt.Sprintf("%s_%s", symbol, timeframe)
e, ok := envMap[envKey]
if !ok {
e = &ta.BarEnv{
TimeFrame: timeframe,
BarNum: 1,
}
envMap[envKey] = e
}
e.OnBar(bar)
ma5 := ta.SMA(e.Close, 5)
ma30 := ta.SMA(e.Close, 30)
atr := ta.ATR(e.High, e.Low, e.Close, 14).Get(0)
xnum := ta.Cross(ma5, ma30)
if xnum == 1 {
// ma5 cross up ma30
curPrice := e.Close.Get(0) // or bar.Close
stopLoss := curPrice - atr
fmt.Printf("open long at %f, stoploss: %f", curPrice, stopLoss)
} else if xnum == -1 {
// ma5 cross down ma30
curPrice := e.Close.Get(0)
fmt.Printf("close long at %f", curPrice)
}
kdjRes := ta.KDJ(e.High, e.Low, e.Close, 9, 3, 3).Cols
k, d := kdjRes[0], kdjRes[1]
}
Core Concept
Traditional technical analysis libraries like TA-Lib and Pandas-TA are widely used and highly optimized for performance, making them extremely fast when computing hundreds or thousands of candles at once.
However, when your trading bot uses these libraries in live trading, every new candle requires passing in hundreds of historical candles. If you're running multiple symbols or operating on 1-minute or even 1-second timeframes, the computational delay can become unbearable.
Many are familiar with TradingView, which uses Pine Script—an event-driven technical analysis engine. It doesn't recalculate historical candles upon receiving a new one but instead reuses cached results.
This is the philosophy behind BanTA: event-driven computation, processing each candle as it arrives while leveraging cached results.
How State Caching Works in BanTA
In BanTA, state caching revolves around the Series sequence type. Most return values are sequences, and the Series struct includes a Data []float64 field that records the indicator's values across candles.
For example, e.Close is the closing price sequence, and e.Close.Get(0) retrieves the current closing price as a float64.
Calculating a moving average is straightforward: ma5 := ta.SMA(e.Close, 5), which returns another sequence.
Some indicators like KDJ return multiple fields: kdjRes := ta.KDJ(e.High, e.Low, e.Close, 9, 3, 3).Cols, where Cols contains an array of sequences (e.g., K and D lines).
How to Use (Parallel Computation)
import (
"github.com/banbox/banta/tav"
)
func main(){
highArr := []float64{1.01, 1.01, 1.02, 0.996, 0.98, 0.993, 0.99, 1.0, 1.02}
lowArr := []float64{0.99, 1.0, 1.0, 0.98, 0.965, 0.98, 0.98, 0.984, 1.0}
closeArr := []float64{1.0, 1.01, 1.0, 0.99, 0.97, 0.981, 0.988, 0.992, 1.002}
sma := tav.SMA(closeArr, 5)
ma30 := tav.SMA(closeArr, 30)
atr := tav.ATR(highArr, lowArr, closeArr, 14)
xArr := tav.Cross(ma5, ma30)
}
Note
For research purposes, we recommend using parallel computation to compute indicators in bulk. For live trading or event-driven backtesting, state-cached indicators offer higher efficiency.
Python Installation
pip install bbta
Only supports Python 8 and above. Currently not compatible with Python 13 on macOS and Windows.
Python Usage (State-Caching Mode)
from bbta import ta
# 1. Create an environment
# BarEnv manages state; create one for each time frame/trading pair.
env = ta.BarEnv(TimeFrame="1m")
# 2. Prepare candle data
# (timestamp ms, open, high, low, close, volume)
klines = [
(1672531200000, 100, 102, 99, 101, 1000), (1672531260000, 101, 103, 100, 102, 1200),
(1672531320000, 102, 105, 101, 104, 1500), (1672531380000, 104, 105, 103, 103, 1300),
(1672531440000, 103, 104, 102, 103, 1100), (1672531500000, 103, 106, 103, 105, 1600),
(1672531560000, 105, 107, 104, 106, 1800), (1672531620000, 106, 106, 102, 103, 2000),
(1672531680000, 103, 104, 101, 102, 1700), (1672531740000, 102, 103, 100, 101, 1400),
]
# 3. Simulate candle pushes
# In live trading, call OnBar for each new candle.
for kline in klines:
ts, o, h, l, c, v = kline
env.OnBar(ts, o, h, l, c, v, 0)
# 4. Calculate indicators
ma5 = ta.Series(ta.SMA(env.Close, 5))
ma30 = ta.Series(ta.SMA(env.Close, 30))
# Get the latest value
ma5_val = ma5.Get(0)
ma30_val = ma30.Get(0)
print(f"Close={c:.2f}, MA5={ma5_val:.2f}, MA30={ma30_val:.2f}")
## Python Usage (Parallel Computation)
```python
from bbta import tav, go
# 1. Prepare data
# Functions in parallel mode accept go.Slice_float64 type.
# We can create it from a Python list.
high_py = [102.0, 103.0, 105.0, 105.0, 104.0, 106.0, 107.0, 106.0, 104.0, 103.0]
low_py = [99.0, 100.0, 101.0, 103.0, 102.0, 103.0, 104.0, 102.0, 101.0, 100.0]
close_py = [101.0, 102.0, 104.0, 103.0, 103.0, 105.0, 106.0, 103.0, 102.0, 101.0]
high = go.Slice_float64(high_py)
low = go.Slice_float64(low_py)
close = go.Slice_float64(close_py)
# 2. Compute all indicators at once
# The result is also a go.Slice type.
ma5 = tav.SMA(close, 5)
atr = tav.ATR(high, low, close, 14)
# 3. View the results
# You can convert it to a Python list to view.
print(f"Close: {list(close)[-5:]}")
print(f"MA5: {[f'{x:.2f}' for x in list(ma5)[-5:]]}")
print(f"ATR: {[f'{x:.2f}' for x in list(atr)[-5:]]}")
# For indicators with multiple return values, like KDJ
kdj_result = tav.KDJ(high, low, close, 9, 3, 3)
k_line = kdj_result[0]
d_line = kdj_result[1]
j_line = kdj_result[2]
print(f"K-line: {[f'{x:.2f}' for x in list(k_line)[-5:]]}")
Documentation
¶
Index ¶
- Constants
- Variables
- func Aroon(high *Series, low *Series, period int) (*Series, *Series, *Series)
- func BBANDS(obj *Series, period int, stdUp, stdDn float64) (*Series, *Series, *Series)
- func Cross(obj1 *Series, obj2 interface{}) intdeprecated
- func HeikinAshi(e *BarEnv) (*Series, *Series, *Series, *Series)
- func KDJ(high *Series, low *Series, close *Series, period int, sm1 int, sm2 int) (*Series, *Series, *Series)
- func KDJBy(high *Series, low *Series, close *Series, period int, sm1 int, sm2 int, ...) (*Series, *Series, *Series)
- func MACD(obj *Series, fast int, slow int, smooth int) (*Series, *Series)
- func MACDBy(obj *Series, fast int, slow int, smooth int, initType int) (*Series, *Series)
- func ParseTimeFrame(timeframe string) (int, error)
- func PluMinDI(high *Series, low *Series, close *Series, period int) (*Series, *Series)
- func PluMinDM(high *Series, low *Series, close *Series, period int) (*Series, *Series)
- func RemoveFromArr[T comparable](arr []T, it T, num int) []T
- func StdDevBy(obj *Series, period int, ddof int) (*Series, *Series)
- func StochRSI(obj *Series, rsiLen int, stochLen int, maK int, maD int) (*Series, *Series)
- func WrapFloatArr(res *Series, period int, inVal float64) []float64
- type BarEnv
- func (e *BarEnv) BarCount(start int64) float64
- func (e *BarEnv) Clone() *BarEnv
- func (e *BarEnv) NewSeries(data []float64) *Series
- func (e *BarEnv) OnBar(barMs int64, open, high, low, close, volume, info float64) error
- func (e *BarEnv) OnBar2(barMS, endMS int64, open, high, low, close, volume, info float64)
- func (e *BarEnv) Reset()
- func (e *BarEnv) ResetTo(env *BarEnv)
- func (e *BarEnv) TrimOverflow()
- type CCentre
- type CGraph
- func (c *CGraph) AddBar(e *BarEnv) *CGraph
- func (c *CGraph) AddBars(barId int, bars ...*Kline) *CGraph
- func (c *CGraph) AddPen(pen *CPen)
- func (c *CGraph) AddSeg(seg *CSeg)
- func (c *CGraph) Bar(v int) *Kline
- func (c *CGraph) Dump() []*DrawLine
- func (c *CGraph) NewPen(a, b *CPoint) *CPen
- func (c *CGraph) NewPoint(dirt float64, price float64, barId int) *CPoint
- func (c *CGraph) Parse()
- func (c *CGraph) Remove(o interface{}) bool
- type CPen
- type CPoint
- type CSeg
- type CTrend
- type CTwoPoint
- type CrossLog
- type DrawLine
- type Kline
- type Series
- func ADL(env *BarEnv) *Series
- func ADX(high *Series, low *Series, close *Series, period int) *Series
- func ADXBy(high *Series, low *Series, close *Series, period, smoothing, method int) *Series
- func ALMA(obj *Series, period int, sigma, distOff float64) *Series
- func ATR(high *Series, low *Series, close *Series, period int) *Series
- func AvgDev(obj *Series, period int) *Series
- func AvgPrice(e *BarEnv) *Series
- func CCI(obj *Series, period int) *Series
- func CHOP(e *BarEnv, period int) *Series
- func CMF(env *BarEnv, period int) *Series
- func CMO(obj *Series, period int) *Series
- func CMOBy(obj *Series, period int, maType int) *Series
- func CRSI(obj *Series, period, upDn, roc int) *Series
- func CRSIBy(obj *Series, period, upDn, roc, vtype int) *Series
- func CTI(obj *Series, period int) *Series
- func ChaikinOsc(env *BarEnv, shortLen int, longLen int) *Series
- func DV2(h, l, c *Series, period, maLen int) *Series
- func EMA(obj *Series, period int) *Series
- func EMABy(obj *Series, period int, initType int) *Series
- func ER(obj *Series, period int) *Series
- func HL2(h, l *Series) *Series
- func HLC3(h, l, c *Series) *Series
- func HMA(obj *Series, period int) *Series
- func Highest(obj *Series, period int) *Series
- func HighestBar(obj *Series, period int) *Series
- func KAMA(obj *Series, period int) *Series
- func KAMABy(obj *Series, period int, fast, slow int) *Series
- func LinReg(obj *Series, period int) *Series
- func LinRegAdv(obj *Series, period int, angle, intercept, degrees, r, slope, tsf bool) *Series
- func Lowest(obj *Series, period int) *Series
- func LowestBar(obj *Series, period int) *Series
- func MFI(e *BarEnv, period int) *Series
- func PercentRank(obj *Series, period int) *Series
- func RMA(obj *Series, period int) *Series
- func RMABy(obj *Series, period int, initType int, initVal float64) *Series
- func RMI(obj *Series, period int, montLen int) *Series
- func ROC(obj *Series, period int) *Series
- func RSI(obj *Series, period int) *Series
- func RSI50(obj *Series, period int) *Series
- func SMA(obj *Series, period int) *Series
- func STC(obj *Series, period, fast, slow int, alpha float64) *Series
- func StdDev(obj *Series, period int) *Series
- func Stiffness(obj *Series, maLen, stiffLen, stiffMa int) *Series
- func Stoch(high, low, close *Series, period int) *Series
- func Sum(obj *Series, period int) *Series
- func TD(obj *Series) *Series
- func TR(high *Series, low *Series, close *Series) *Series
- func UTBot(c, atr *Series, rate float64) *Series
- func UpDown(obj *Series, vtype int) *Series
- func VWMA(price *Series, vol *Series, period int) *Series
- func WMA(obj *Series, period int) *Series
- func WillR(e *BarEnv, period int) *Series
- func (s *Series) Abs() *Series
- func (s *Series) Add(obj interface{}) *Series
- func (s *Series) Append(obj interface{}) *Series
- func (s *Series) Back(num int) *Series
- func (s *Series) Cached() bool
- func (s *Series) CopyTo(e *BarEnv) *Series
- func (s *Series) Cross(obj2 interface{}) int
- func (s *Series) Cut(keepNum int)
- func (s *Series) Div(obj interface{}) *Series
- func (s *Series) Get(i int) float64
- func (s *Series) Len() int
- func (s *Series) Max(obj interface{}) *Series
- func (s *Series) Min(obj interface{}) *Series
- func (s *Series) Mul(obj interface{}) *Series
- func (s *Series) Range(start, stop int) []float64
- func (s *Series) RangeValid(start, stop int) ([]float64, []int)
- func (s *Series) Set(obj interface{}) *Series
- func (s *Series) Sub(obj interface{}) *Series
- func (s *Series) To(k string, v int) *Series
- type XState
Constants ¶
const ( MinPenLen = 5 CLInit = 0 // 尚未确认有效 CLValid = 1 // 确认有效，尚未完成 CLDone = 2 // 确认有效，已完成，不会再改 ParseLevelPen = 1 ParseLevelSeg = 2 ParseLevelCentre = 3 ParseLevelTrend = 4 DefMaxPen = 1000 // 默认保存的最大笔数量 )
const ( EvtNew = 1 EvtChange = 2 EvtRemove = 3 )
Variables ¶
var (
ErrInvalidSeriesVal = errors.New("invalid val for Series")
)
Functions ¶
func Aroon ¶ added in v0.1.3
Aroon 阿隆指标
Reflects the distance between the highest price and the lowest price within a certain period of time. 反映了一段时间内出现最高价和最低价距离当前时间的远近。
AroonUp: (period - HighestBar(high, period+1)) / period * 100
AroonDn: (period - LowestBar(low, period+1)) / period * 100
Osc: AroonUp - AroonDn
return [AroonUp, Osc, AroonDn]
func BBANDS ¶
BBANDS Bollinger Bands 布林带指标
period: 20, stdUp: 2, stdDn: 2
return [upper, mid, lower]
func HeikinAshi ¶
HeikinAshi return [open,high,low,close]
func KDJ ¶
func KDJ(high *Series, low *Series, close *Series, period int, sm1 int, sm2 int) (*Series, *Series, *Series)
KDJ alias: stoch indicator;
period: 9, sm1: 3, sm2: 3
return (K, D, RSV)
func KDJBy ¶
func KDJBy(high *Series, low *Series, close *Series, period int, sm1 int, sm2 int, maBy string) (*Series, *Series, *Series)
KDJBy alias: talib stoch indicator;
period: 9, sm1: 3, sm2: 3
return (K, D, RSV)
func MACD ¶
MACD
Internationally, init_type=0 is used, while MyTT and China mainly use init_type=1 国外主流使用init_type=0，MyTT和国内主要使用init_type=1
fast: 12, slow: 26, smooth: 9
return [macd, signal]
func ParseTimeFrame ¶ added in v0.3.5
func RemoveFromArr ¶ added in v0.1.3
func RemoveFromArr[T comparable](arr []T, it T, num int) []T
Types ¶
type BarEnv ¶
type BarEnv struct {
TimeStart int64
TimeStop int64
Exchange string
MarketType string
Symbol string
TimeFrame string
TFMSecs int64 //周期的毫秒间隔
BarNum int
MaxCache int
VNum int
Open *Series
High *Series
Low *Series
Close *Series
Volume *Series
Info *Series
Data sync.Map // map[string]interface{}
Items map[int]*Series
Lock sync.Mutex // 用户手动控制的锁，用于保护并发访问
}
func (*BarEnv) TrimOverflow ¶
func (e *BarEnv) TrimOverflow()
type CCentre ¶ added in v0.1.3
type CCentre struct {
*CTwoPoint
Overlap [2]float64 // 中枢重叠区间
Range [2]float64 // 中枢高低区间
Dirt float64
}
中枢
type CGraph ¶ added in v0.1.3
type CGraph struct {
Pens []*CPen
Segs []*CSeg
Trends []*CTrend
Bars []*Kline
Centres []*CCentre
OnPoint func(p *CPoint, evt int)
OnPen func(p *CPen, evt int)
OnSeg func(p *CSeg, evt int)
OnCentre func(c *CCentre, evt int)
BarNum int // 最后一个bar的序号，从1开始
ParseLevel int // 期望解析到的等级，0不限制
ParseTo int // 解析到K线的位置，序号，非索引
MaxPen int // 保存最大笔数量，默认1000
// contains filtered or unexported fields
}
type CPoint ¶ added in v0.1.3
type CPoint struct {
Graph *CGraph
Dirt float64 // 1顶分型，-1底分型
BarId int // 此bar的序号
Price float64 // 价格
StartPen *CPen
EndPen *CPen
StartSeg *CSeg
EndSeg *CSeg
StartTrend *CTrend
EndTrend *CTrend
Next *CPoint
}
type CSeg ¶ added in v0.1.3
type CSeg struct {
*CTwoPoint
Dirt float64 // 1向上 0不确定 -1向下
Feas [][2]float64 // 特征序列
InForce bool // 标记此线段是否必须以顶底分型确认
Temp bool // 标记此线段是假设的，需要再次检查是否有效
Prev *CSeg
Next *CSeg
Centre *CCentre // 此线段所属中枢，只对中间部分线段设置；一个线段只可属于一个中枢
}
线段
func (*CSeg) AddFeature ¶ added in v0.1.3
AddFeature 将笔作为特征序列
func (*CSeg) CalcFeatures ¶ added in v0.1.3
func (s *CSeg) CalcFeatures()
CalcFeatures 重新计算线段的所有特征序列，当线段结束点修改时调用此方法
func (*CSeg) SetLastFea ¶ added in v0.1.3
type Series ¶
type Series struct {
ID int
Env *BarEnv
Data []float64
Cols []*Series
Time int64
More interface{}
DupMore func(interface{}) interface{}
Subs map[string]map[int]*Series // 由此序列派生的；function：hash：object
XLogs map[int]*CrossLog // 此序列交叉记录
}
func ADXBy ¶ added in v0.1.3
ADXBy Average Directional Index
method=0 classic ADX method=1 TradingView "ADX and DI for v4"
suggest period: 14 smoothing: 0 to use period
return maDX
func ALMA ¶ added in v0.1.6
ALMA Arnaud Legoux Moving Average
period: window size. Default: 10
sigma: Smoothing value. Default 6.0
distOff: min 0 (smoother), max 1 (more responsive). Default: 0.85
func CCI ¶ added in v0.1.3
CCI Commodity Channel Index
https://www.tradingview.com/support/solutions/43000502001-commodity-channel-index-cci/
suggest period: 20
func CHOP ¶ added in v0.1.6
CHOP Choppiness Index
suggest period: 14
higher values equal more choppiness, while lower values indicate directional trending. 值越高，波动性越大，而值越低，则表示有方向性趋势。
func CMF ¶ added in v0.1.3
CMF Chaikin Money Flow
https://www.tradingview.com/scripts/chaikinmoneyflow/?solution=43000501974
suggest period: 20
func CMO ¶ added in v0.1.6
CMO Chande Momentum Oscillator
suggest period: 9
Same implementation as ta-lib For TradingView, use: CMOBy(obj, period, 1)
func CMOBy ¶ added in v0.1.6
CMOBy Chande Momentum Oscillator
suggest period: 9
maType: 0: ta-lib 1: tradingView
func CRSI ¶ added in v0.1.8
CRSI Connors RSI
suggest period:3, upDn:2, roc:100
Basically the same as TradingView
func CRSIBy ¶ added in v0.1.8
CRSIBy Connors RSI
suggest period:3, upDn:2, roc:100
vtype: 0 Calculation in TradingView method
1 Calculation in ta-lib community method:
chg = close_col / close_col.shift(1) updown = np.where(chg.gt(1), 1.0, np.where(chg.lt(1), -1.0, 0.0)) rsi = ta.RSI(close_arr, timeperiod=3) ud = ta.RSI(updown, timeperiod=2) roc = ta.ROC(close_arr, 100) crsi = (rsi + ud + roc) / 3
func CTI ¶ added in v0.1.4
CTI Correlation Trend Indicator
The Correlation Trend Indicator is an oscillator created by John Ehler in 2020. It assigns a value depending on how close prices in that range are to following a positively- or negatively-sloping straight line. Values range from -1 to 1. This is a wrapper for LinRegAdv.
suggest period: 20
func ChaikinOsc ¶ added in v0.1.3
ChaikinOsc Chaikin Oscillator
https://www.tradingview.com/support/solutions/43000501979-chaikin-oscillator/
short: 3, long: 10
func DV2 ¶ added in v0.1.10
DV2 Developed by David Varadi of http://cssanalytics.wordpress.com/
period: 252 maLen: 2 This seems to be the *Bounded* version. 这里和backtrader中实现不一致，是另一个版本。 See also: - http://web.archive.org/web/20131216100741/http://quantingdutchman.wordpress.com/2010/08/06/dv2-indicator-for-amibroker/ - https://www.reddit.com/r/CapitalistExploits/comments/1d0azms/david_varadis_dv2_indicator_trading_strategies/
func HighestBar ¶ added in v0.1.3
func KAMA ¶ added in v0.1.3
KAMA Kaufman Adaptive Moving Average
period: 10 fixed: (fast: 2, slow: 30)
func LinReg ¶ added in v0.1.4
LinReg Linear Regression Moving Average
Linear Regression Moving Average (LINREG). This is a simplified version of a Standard Linear Regression. LINREG is a rolling regression of one variable. A Standard Linear Regression is between two or more variables.
func PercentRank ¶ added in v0.1.8
PercentRank
calculates the percentile rank of a bar value in a data set.
func RMA ¶
RMA Relative Moving Average 相对移动均线
The difference from EMA is: both the numerator and denominator are reduced by 1 Latest value weight: 1/n
和EMA区别是：分子分母都减一 最近一个权重：1/n
func RMABy ¶
RMABy Relative Moving Average 相对移动均线
The difference from EMA is: both the numerator and denominator are reduced by 1 The most recent weight: 1/n
和EMA区别是：分子分母都减一 最近一个权重：1/n
initType: 0 initialize with SMA, 1 initialize with the first valid value
initVal defaults to Nan
initType：0使用SMA初始化，1第一个有效值初始化
initVal 默认Nan
func RMI ¶ added in v0.1.3
RMI Relative Momentum Index https://theforexgeek.com/relative-momentum-index/
period: 14, montLen: 3
func Stoch ¶ added in v0.1.3
Stoch 100 * (close - lowest(low, period)) / (highest(high, period) - lowest(low, period))
use KDJ if you want to apply SMA/RMA to this
suggest period: 14
func UpDown ¶ added in v0.1.8
UpDown
vtype: 0 TradingView (Count consecutive times)
1 classic (abs count up to 1)
func VWMA ¶ added in v0.1.3
VWMA Volume Weighted Moving Average 成交量加权平均价格
sum(price*volume)/sum(volume)
suggest period: 20
func WMA ¶ added in v0.1.4
WMA Weighted Moving Average.
the weighting factors decrease in arithmetic progression.
suggest period: 9
func (*Series) Cross ¶ added in v0.3.1
Cross 计算最近一次交叉的距离。比较对象必须是常数或Series对象 返回值：正数上穿，负数下穿，0表示未知或重合；abs(ret) - 1表示交叉点与当前bar的距离
func (*Series) RangeValid ¶ added in v0.3.0
RangeValid 获取范围内的值，跳过nan。 start 起始位置，0是最近的 stop 结束位置，不含
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