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

[NeurIPS 2021] “Improving Contrastive Learning on Imbalanced Data via Open-World Sampling”,

Improving Contrastive Learning on Imbalanced Data via Open-World Sampling Introduction Contrastive learning approaches have achieved great success in

VITA 24 Dec 17, 2022
TyXe: Pyro-based BNNs for Pytorch users

TyXe: Pyro-based BNNs for Pytorch users TyXe aims to simplify the process of turning Pytorch neural networks into Bayesian neural networks by leveragi

87 Jan 03, 2023
[Arxiv preprint] Causality-inspired Single-source Domain Generalization for Medical Image Segmentation (code&data-processing pipeline)

Causality-inspired Single-source Domain Generalization for Medical Image Segmentation Arxiv preprint Repository under construction. Might still be bug

Cheng 31 Dec 27, 2022
Re-implememtation of MAE (Masked Autoencoders Are Scalable Vision Learners) using PyTorch.

mae-repo PyTorch re-implememtation of "masked autoencoders are scalable vision learners". In this repo, it heavily borrows codes from codebase https:/

Peng Qiao 1 Dec 14, 2021
Mini Software that give reminder to drink water as per your weight.

Water Notification Desktop Python The Mini Software built in Python (tkinter) that will remind you to drink water on specific time span based on your

Om Jogani 5 Dec 16, 2022
Official PyTorch Implementation of Convolutional Hough Matching Networks, CVPR 2021 (oral)

Convolutional Hough Matching Networks This is the implementation of the paper "Convolutional Hough Matching Network" by J. Min and M. Cho. Implemented

Juhong Min 70 Nov 22, 2022
This repository is to support contributions for tools for the Project CodeNet dataset hosted in DAX

The goal of Project CodeNet is to provide the AI-for-Code research community with a large scale, diverse, and high quality curated dataset to drive innovation in AI techniques.

International Business Machines 1.2k Jan 04, 2023
Repo for "TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets" at [email protected]

TableParser Repo for "TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets" at DS3 Lab 11 Dec 13, 2022

A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).

GAM ⠀⠀ A PyTorch implementation of Graph Classification Using Structural Attention (KDD 2018). Abstract Graph classification is a problem with practic

Benedek Rozemberczki 259 Dec 05, 2022
PyTorch implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

Anomaly Transformer in PyTorch This is an implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy. This pape

spencerbraun 160 Dec 19, 2022
The code written during my Bachelor Thesis "Classification of Human Whole-Body Motion using Hidden Markov Models".

This code was written during the course of my Bachelor thesis Classification of Human Whole-Body Motion using Hidden Markov Models. Some things might

Matthias Plappert 14 Dec 06, 2022
Code for the ICCV 2021 paper "Pixel Difference Networks for Efficient Edge Detection" (Oral).

Microsoft365_devicePhish Abusing Microsoft 365 OAuth Authorization Flow for Phishing Attack This is a simple proof-of-concept script that allows an at

Alex 236 Dec 21, 2022
A testcase generation tool for Persistent Memory Programs.

PMFuzz PMFuzz is a testcase generation tool to generate high-value tests cases for PM testing tools (XFDetector, PMDebugger, PMTest and Pmemcheck) If

Systems Research at ShiftLab 14 Jul 24, 2022
This repository contains part of the code used to make the images visible in the article "How does an AI Imagine the Universe?" published on Towards Data Science.

Generative Adversarial Network - Generating Universe This repository contains part of the code used to make the images visible in the article "How doe

Davide Coccomini 9 Dec 18, 2022
Research - dataset and code for 2016 paper Learning a Driving Simulator

the people's comma the paper Learning a Driving Simulator the comma.ai driving dataset 7 and a quarter hours of largely highway driving. Enough to tra

comma.ai 4.1k Jan 02, 2023
Face uncertainty quantification or estimation using PyTorch.

Face-uncertainty-pytorch This is a demo code of face uncertainty quantification or estimation using PyTorch. The uncertainty of face recognition is af

Kaen 3 Sep 16, 2022
L-Verse: Bidirectional Generation Between Image and Text

Far beyond learning long-range interactions of natural language, transformers are becoming the de-facto standard for many vision tasks with their power and scalabilty

Kim, Taehoon 102 Dec 21, 2022
RodoSol-ALPR Dataset

RodoSol-ALPR Dataset This dataset, called RodoSol-ALPR dataset, contains 20,000 images captured by static cameras located at pay tolls owned by the Ro

Rayson Laroca 45 Dec 15, 2022
Various operations like path tracking, counting, etc by using yolov5

Object-tracing-with-YOLOv5 Various operations like path tracking, counting, etc by using yolov5

Pawan Valluri 5 Nov 28, 2022
PyTorch Code for the paper "VSE++: Improving Visual-Semantic Embeddings with Hard Negatives"

Improving Visual-Semantic Embeddings with Hard Negatives Code for the image-caption retrieval methods from VSE++: Improving Visual-Semantic Embeddings

Fartash Faghri 441 Dec 05, 2022