A python library for computing technical analysis indicators on streaming data.
pip install streaming-indicators
There are many other technical analysis python packages, most notably ta-lib, then why another library?
All other libraries work on static data, you can not add values to any indicator. But in real-time trading system, price values (ticks/candles) keeps streaming, and indicators should update on real-time. This library is for that purpose.
Each indicator is a class, and is statefull. It will have 3 main functions:
- Constructor: initialise all parameters such as period.
- update: To add new data point in the indicator computation. Returns the new value of the indicator.
- compute: Compute indicator value with a new data point, but don't update it's state. This is useful in some cases, for example, compute indictor on ltp, but don't update it.
- Simple Moving Average (SMA)
import streaming_indicators as si
period = 14
SMA = si.SMA(period)
for idx, candle in candles.iterrows():
sma = SMA.update(candle['close'])
print(sma)
- Exponential Moving Average (EMA)
period = 14
EMA = si.EMA(period)
for idx, candle in candles.iterrows():
ema = EMA.update(candle['close'])
print(ema)
- Weighted Moving Average (WMA)
- Smoothed Moving Average (SMMA)
- Relative Strength Index (RSI)
period = 14
RSI = si.RSI(period)
for idx, candle in candles.iterrows():
rsi = RSI.update(candle['close'])
print(rsi)
- Central Pivot Range (CPR)
- True Range (TRANGE)
- Average True Range (ATR)
atr_period = 20
ATR = si.ATR(atr_period)
for idx, candle in candles.iterrows():
atr = ATR.update(candle) # Assumes candle to have 'open',high','low','close' - TODO: give multiple inputs to update.
print(atr)
- Bollinger Bands (BBands)
- SuperTrend (SuperTrend)
st_atr_length = 10
st_factor = 3
ST = si.SuperTrend(st_atr_length, st_factor)
for idx, candle in candles.iterrows():
st = ST.update(candle)
print(st) # (st_direction:1/-1, band_value)
To use some historical candles to initiate, use: ST = si.SuperTrend(st_atr_length, st_factor, candles=initial_candles) where initial_candles is pandas dataframe with open,high,low,close columns, and requires talib package.
- Heikin Ashi Candlesticks (HeikinAshi)
HA = si.HeikinAshi()
for idx, candle in candles.iterrows():
ha_candle = HA.update(candle)
print(ha_candle) # {'close': float, 'open': float, 'high': float, 'low': float}
- Renko Bricks (Renko)
# For fixed brick size
brick_size = 20
Renko = si.Renko()
for idx, candle in candles.iterrows():
bricks = Renko.update(candle['close'], brick_size)
print(bricks) # [{'direction': 1/-1, 'brick_num': int, 'wick_size': float, 'brick_size': float, 'brick_end_price': float, 'price': float}, {}]: list of bricks formed after this candle
# For brick size using ATR
atr_period = 20
ATR = si.ATR(atr_period)
Renko = si.Renko()
for idx, candle in candles.iterrows():
atr = ATR.update(candle)
print(atr)
bricks = Renko.update(candle['close'], atr)
print(bricks)
- Order Checking (IsOrder)
Checks if the running sequence is in a given order, eg increasing, decreasing, exponential, etc. Useful when checking if consecutive n candles/ltps were increasing.
period = 10
all_increasing = si.IsOrder('>', period)
for idx, candle in candles.iterrows():
is_increasing = all_increasing.update(candle['close'])
print(is_increasing) # True/False
- HalfTrend (
HalfTrend)
HalfTrend indicator by Alex Orekhov (everget) in tradingview. Refered it's pine script.trend = 0for uptrend and1for downtrend.
HT = si.HalfTrend(amplitude=2, channel_deviation=2, atr_period=100)
for idx, candle in candles.iterrows():
trend, half_trend, up, down, atr_high, atr_low = HT.update(candle)
- CWA 2-Sigma (
CWA2Sigma)
As discussed by Mr Rakesh Pujara in his interview.
CWA2Sigma = si.CWA2Sigma(bb_period=50, bb_width=2, ema_period=100, atr_period=14, atr_factor=1.8, sl_perc=20)
for idx, candle in candles.iterrows():
cwa_signal,cwa_entry_price = CWA2Sigma.update(candle)
If you find this repo useful, do consider giving a star. Contributions are most welcome.