Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! Very tiny! Stock Market Financial Technical Analysis Python library . Quant Trading automation or cryptocoin exchange

Overview

MyTT

Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! to Stock Market Financial Technical Analysis Python library MyTT.py

Features

  • Innovative application of core tools function,so to writing indicator becomes easy and interesting!
  • Calculate technical indicators (Most of the indicators supported)
  • Produce graphs for any technical indicator
  • MyTT is very very fast! pure numpy and pandas implemented, not need install Ta-lib (talib)
  • MyTT is very simple,only use numpy and pandas even not "for in " in the code
  • Trading automation Quant Trade, Stock Market, Futures market or cryptocoin exchange like BTC
  • Chinese version MyTT Url: https://github.com/mpquant/MyTT
#  ----- 0 level:core tools function ---------

 def MA(S,N):                          
    return pd.Series(S).rolling(N).mean().values   

 def DIFF(S, N=1):         
    return pd.Series(S).diff(N)  
    
 def STD(S,N):              
    return  pd.Series(S).rolling(N).std(ddof=0).values

 def EMA(S,N):               # alpha=2/(span+1)    
    return pd.Series(S).ewm(span=N, adjust=False).mean().values  

 def SMA(S, N, M=1):        #   alpha=1/(1+com)
    return pd.Series(S).ewm(com=N-M, adjust=True).mean().values     

 def AVEDEV(S,N):          
    return pd.Series(S).rolling(N).apply(lambda x: (np.abs(x - x.mean())).mean()).values 

 def IF(S_BOOL,S_TRUE,S_FALSE):  
    return np.where(S_BOOL, S_TRUE, S_FALSE)

 def SUM(S, N):                   
    return pd.Series(S).rolling(N).sum().values if N>0 else pd.Series(S).cumsum()  

 def HHV(S,N):                   
    return pd.Series(S).rolling(N).max().values     

 def LLV(S,N):            
    return pd.Series(S).rolling(N).min().values    
#-----   1 level: Logic and Statistical function  (only use 0 level function to implemented) -----

def COUNT(S_BOOL, N):                  # COUNT(CLOSE>O, N): 
    return SUM(S_BOOL,N)    

def EVERY(S_BOOL, N):                  # EVERY(CLOSE>O, 5)  
    R=SUM(S_BOOL, N)
    return  IF(R==N, True, False)
  
def LAST(S_BOOL, A, B):                   
    if A<B: A=B                        #LAST(CLOSE>OPEN,5,3)  
    return S_BOOL[-A:-B].sum()==(A-B)    

def EXIST(S_BOOL, N=5):                # EXIST(CLOSE>3010, N=5) 
    R=SUM(S_BOOL,N)    
    return IF(R>0, True ,False)

def BARSLAST(S_BOOL):                  
    M=np.argwhere(S_BOOL);             # BARSLAST(CLOSE/REF(CLOSE)>=1.1) 
    return len(S_BOOL)-int(M[-1])-1  if M.size>0 else -1

def FORCAST(S,N):                      
    K,Y=SLOPE(S,N,RS=True)
    return Y[-1]+K
  
def CROSS(S1,S2):                      # GoldCross CROSS(MA(C,5),MA(C,10))  
    CROSS_BOOL=IF(S1>S2, True ,False)  # DieCross CROSS(MA(C,10),MA(C,5))
    return (COUNT(CROSS_BOOL>0,2)==1)*CROSS_BOOL
# ------ Technical Indicators  ( 2 level only use 0,1 level functions to implemented) --------------

def MACD(CLOSE,SHORT=12,LONG=26,M=9):             
    DIF = EMA(CLOSE,SHORT)-EMA(CLOSE,LONG);  
    DEA = EMA(DIF,M);      MACD=(DIF-DEA)*2
    return DIF,DEA,MACD

def KDJ(CLOSE,HIGH,LOW, N=9,M1=3,M2=3):          
    RSV = (CLOSE - LLV(LOW, N)) / (HHV(HIGH, N) - LLV(LOW, N)) * 100
    K = EMA(RSV, (M1*2-1));    D = EMA(K,(M2*2-1));        J=K*3-D*2
    return K, D, J

def RSI(CLOSE, N=24):                          
    DIF = CLOSE-REF(CLOSE,1) 
    return (SMA(MAX(DIF,0), N) / SMA(ABS(DIF), N) * 100)  

def WR(CLOSE, HIGH, LOW, N=10, N1=6):           
    WR = (HHV(HIGH, N) - CLOSE) / (HHV(HIGH, N) - LLV(LOW, N)) * 100
    WR1 = (HHV(HIGH, N1) - CLOSE) / (HHV(HIGH, N1) - LLV(LOW, N1)) * 100
    return WR, WR1

def BIAS(CLOSE,L1=6, L2=12, L3=24):             
    BIAS1 = (CLOSE - MA(CLOSE, L1)) / MA(CLOSE, L1) * 100
    BIAS2 = (CLOSE - MA(CLOSE, L2)) / MA(CLOSE, L2) * 100
    BIAS3 = (CLOSE - MA(CLOSE, L3)) / MA(CLOSE, L3) * 100
    return BIAS1, BIAS2, BIAS3

def BOLL(CLOSE,N=20, P=2):                          
    MID = MA(CLOSE, N); 
    UPPER = MID + STD(CLOSE, N) * P
    LOWER = MID - STD(CLOSE, N) * P
    return UPPER, MID, LOWER

def PSY(CLOSE,N=12, M=6):  
    PSY=COUNT(CLOSE>REF(CLOSE,1),N)/N*100
    PSYMA=MA(PSY,M)
    return PSY,PSYMA

def CCI(CLOSE,HIGH,LOW,N=14):  
    TP=(HIGH+LOW+CLOSE)/3
    return (TP-MA(TP,N))/(0.015*AVEDEV(TP,N))
        
def ATR(CLOSE,HIGH,LOW, N=20):                    
    TR = MAX(MAX((HIGH - LOW), ABS(REF(CLOSE, 1) - HIGH)), ABS(REF(CLOSE, 1) - LOW))
    return MA(TR, N)

def BBI(CLOSE,M1=3,M2=6,M3=12,M4=20):             
    return (MA(CLOSE,M1)+MA(CLOSE,M2)+MA(CLOSE,M3)+MA(CLOSE,M4))/4    

def DMI(CLOSE,HIGH,LOW,M1=14,M2=6):               
    TR = SUM(MAX(MAX(HIGH - LOW, ABS(HIGH - REF(CLOSE, 1))), ABS(LOW - REF(CLOSE, 1))), M1)
    HD = HIGH - REF(HIGH, 1);     LD = REF(LOW, 1) - LOW
    DMP = SUM(IF((HD > 0) & (HD > LD), HD, 0), M1)
    DMM = SUM(IF((LD > 0) & (LD > HD), LD, 0), M1)
    PDI = DMP * 100 / TR;         MDI = DMM * 100 / TR
    ADX = MA(ABS(MDI - PDI) / (PDI + MDI) * 100, M2)
    ADXR = (ADX + REF(ADX, M2)) / 2
    return PDI, MDI, ADX, ADXR  

  
def TRIX(CLOSE,M1=12, M2=20):                      
    TR = EMA(EMA(EMA(CLOSE, M1), M1), M1)
    TRIX = (TR - REF(TR, 1)) / REF(TR, 1) * 100
    TRMA = MA(TRIX, M2)
    return TRIX, TRMA

def VR(CLOSE,VOL,M1=26):                            
    LC = REF(CLOSE, 1)
    return SUM(IF(CLOSE > LC, VOL, 0), M1) / SUM(IF(CLOSE <= LC, VOL, 0), M1) * 100

def EMV(HIGH,LOW,VOL,N=14,M=9):                     
    VOLUME=MA(VOL,N)/VOL;       MID=100*(HIGH+LOW-REF(HIGH+LOW,1))/(HIGH+LOW)
    EMV=MA(MID*VOLUME*(HIGH-LOW)/MA(HIGH-LOW,N),N);    MAEMV=MA(EMV,M)
    return EMV,MAEMV

def DMA(CLOSE,N1=10,N2=50,M=10):                     
    DIF=MA(CLOSE,N1)-MA(CLOSE,N2);    DIFMA=MA(DIF,M)
    return DIF,DIFMA

def MTM(CLOSE,N=12,M=6):                             
    MTM=CLOSE-REF(CLOSE,N);         MTMMA=MA(MTM,M)
    return MTM,MTMMA

 
def EXPMA(CLOSE,N1=12,N2=50):                       
    return EMA(CLOSE,N1),EMA(CLOSE,N2);

def OBV(CLOSE,VOL):                                 
    return SUM(IF(CLOSE>REF(CLOSE,1),VOL,IF(CLOSE<REF(CLOSE,1),-VOL,0)),0)/10000

Usage Example

from  hb_hq_api import *         #  btc day data on Huobi cryptocoin exchange 
from  MyTT import *              #  to import lib

df=get_price('btc.usdt',count=120,frequency='1d');     #'1d'=1day , '4h'=4hour

#-----------df view-------------------------------------------
open close high low vol
2021-05-16 48983.62 47738.24 49800.00 46500.0 1.333333e+09
2021-05-17 47738.24 43342.50 48098.66 42118.0 3.353662e+09
2021-05-18 43342.50 44093.24 45781.52 42106.0 1.793267e+09
CLOSE=df.close.values     #or  CLOSE=list(df.close)
OPEN =df.open.values           
HIGH =df.high.values    
LOW = df.low.values            

MA5=MA(CLOSE,5)                                       
MA10=MA(CLOSE,10)                                     

RSI12=RSI(CLOSE,12)
CCI12=CCI(CLOSE,12)
ATR20=ATR(CLOSE,HIGH,LOW, N=20)

print('BTC5 MA5', MA5[-1] )                         
print('BTC MA10,RET(MA10))                         # RET(MA10) == MA10[-1]
print('today ma5 coross ma10? ',RET(CROSS(MA5,MA10)))
print('every close price> ma10? ',EVERY(CLOSE>MA10,5) )

BOLL and graphs

up,mid,lower=BOLL(CLOSE)                                       

plt.figure(figsize=(15,8))  
plt.plot(CLOSE,label='shanghai');
plt.plot(up,label='up');        
plt.plot(mid,label='mid'); 
plt.plot(lower,label='lower');
Boll

python lib need to install

  • pandas numpy

You might also like...
Degree-Quant: Quantization-Aware Training for Graph Neural Networks.

Degree-Quant This repo provides a clean re-implementation of the code associated with the paper Degree-Quant: Quantization-Aware Training for Graph Ne

Composable transformations of Python+NumPy programsComposable transformations of Python+NumPy programs

Chex Chex is a library of utilities for helping to write reliable JAX code. This includes utils to help: Instrument your code (e.g. assertions) Debug

MLP-Numpy - A simple modular implementation of Multi Layer Perceptron in pure Numpy.

MLP-Numpy A simple modular implementation of Multi Layer Perceptron in pure Numpy. I used the Iris dataset from scikit-learn library for the experimen

A CNN implementation using only numpy. Supports multidimensional images, stride, etc.

A CNN implementation using only numpy. Supports multidimensional images, stride, etc. Speed up due to heavy use of slicing and mathematical simplification..

Trading Gym is an open source project for the development of reinforcement learning algorithms in the context of trading.

Trading Gym Trading Gym is an open-source project for the development of reinforcement learning algorithms in the context of trading. It is currently

This is a simple backtesting framework to help you test your crypto currency trading. It includes a way to download and store historical crypto data and to execute a trading strategy.

You can use this simple crypto backtesting script to ensure your trading strategy is successful Minimal setup required and works well with static TP a

A general-purpose, flexible, and easy-to-use simulator alongside an OpenAI Gym trading environment for MetaTrader 5 trading platform (Approved by OpenAI Gym)
A general-purpose, flexible, and easy-to-use simulator alongside an OpenAI Gym trading environment for MetaTrader 5 trading platform (Approved by OpenAI Gym)

gym-mtsim: OpenAI Gym - MetaTrader 5 Simulator MtSim is a simulator for the MetaTrader 5 trading platform alongside an OpenAI Gym environment for rein

Neural Magic Eye: Learning to See and Understand the Scene Behind an Autostereogram, arXiv:2012.15692.
Neural Magic Eye: Learning to See and Understand the Scene Behind an Autostereogram, arXiv:2012.15692.

Neural Magic Eye Preprint | Project Page | Colab Runtime Official PyTorch implementation of the preprint paper "NeuralMagicEye: Learning to See and Un

E-Ink Magic Calendar that automatically syncs to Google Calendar and runs off a battery powered Raspberry Pi Zero
E-Ink Magic Calendar that automatically syncs to Google Calendar and runs off a battery powered Raspberry Pi Zero

MagInkCal This repo contains the code needed to drive an E-Ink Magic Calendar that uses a battery powered (PiSugar2) Raspberry Pi Zero WH to retrieve

Owner
dev
dev
This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.

Reinforcement-trading This project uses Reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can

Deepender Singla 1.4k Dec 22, 2022
A Deep Reinforcement Learning Framework for Stock Market Trading

DQN-Trading This is a framework based on deep reinforcement learning for stock market trading. This project is the implementation code for the two pap

null 61 Jan 1, 2023
A very tiny, very simple, and very secure file encryption tool.

Picocrypt is a very tiny (hence "Pico"), very simple, yet very secure file encryption tool. It uses the modern ChaCha20-Poly1305 cipher suite as well

Evan Su 1k Dec 30, 2022
:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

bulbea "Deep Learning based Python Library for Stock Market Prediction and Modelling." Table of Contents Installation Usage Documentation Dependencies

Achilles Rasquinha 1.8k Jan 5, 2023
This project provides a stock market environment using OpenGym with Deep Q-learning and Policy Gradient.

Stock Trading Market OpenAI Gym Environment with Deep Reinforcement Learning using Keras Overview This project provides a general environment for stoc

Kim, Ki Hyun 769 Dec 25, 2022
A resource for learning about deep learning techniques from regression to LSTM and Reinforcement Learning using financial data and the fitness functions of algorithmic trading

A tour through tensorflow with financial data I present several models ranging in complexity from simple regression to LSTM and policy networks. The s

null 195 Dec 7, 2022
Use deep learning, genetic programming and other methods to predict stock and market movements

StockPredictions Use classic tricks, neural networks, deep learning, genetic programming and other methods to predict stock and market movements. Both

Linda MacPhee-Cobb 386 Jan 3, 2023
This project is a loose implementation of paper "Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach"

Stock Market Buy/Sell/Hold prediction Using convolutional Neural Network This repo is an attempt to implement the research paper titled "Algorithmic F

Asutosh Nayak 136 Dec 28, 2022
Forecasting directional movements of stock prices for intraday trading using LSTM and random forest

Forecasting directional movements of stock-prices for intraday trading using LSTM and random-forest https://arxiv.org/abs/2004.10178 Pushpendu Ghosh,

Pushpendu Ghosh 270 Dec 24, 2022
Stock-history-display - something like a easy yearly review for your stock performance

Stock History Display Available on Heroku: https://stock-history-display.herokua

LiaoJJ 1 Jan 7, 2022