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

This is an official source code for implementation on Extensive Deep Temporal Point Process

Extensive Deep Temporal Point Process This is an official source code for implementation on Extensive Deep Temporal Point Process, which is composed o

Haitao Lin 8 Aug 15, 2022
Code release for NeurIPS 2020 paper "Co-Tuning for Transfer Learning"

CoTuning Official implementation for NeurIPS 2020 paper Co-Tuning for Transfer Learning. [News] 2021/01/13 The COCO 70 dataset used in the paper is av

THUML @ Tsinghua University 35 Sep 23, 2022
Selfplay In MultiPlayer Environments

This project allows you to train AI agents on custom-built multiplayer environments, through self-play reinforcement learning.

200 Jan 08, 2023
Code and training data for our ECCV 2016 paper on Unsupervised Learning

Shuffle and Learn (Shuffle Tuple) Created by Ishan Misra Based on the ECCV 2016 Paper - "Shuffle and Learn: Unsupervised Learning using Temporal Order

Ishan Misra 44 Dec 08, 2021
A python library for time-series smoothing and outlier detection in a vectorized way.

tsmoothie A python library for time-series smoothing and outlier detection in a vectorized way. Overview tsmoothie computes, in a fast and efficient w

Marco Cerliani 517 Dec 28, 2022
Everything about being a TA for ITP/AP course!

تی‌ای بودن! تی‌ای یا دستیار استاد از نقش‌های رایج بین دانشجویان مهندسی است، این ریپوزیتوری قرار است نکات مهم درمورد تی‌ای بودن و تی ای شدن را به ما نش

<a href=[email protected]"> 14 Sep 10, 2022
Dahua Camera and Doorbell Home Assistant Integration

Home Assistant Dahua Integration The Dahua Home Assistant integration allows you to integrate your Dahua cameras and doorbells in Home Assistant. It's

Ronnie 216 Dec 26, 2022
Chess reinforcement learning by AlphaGo Zero methods.

About Chess reinforcement learning by AlphaGo Zero methods. This project is based on these main resources: DeepMind's Oct 19th publication: Mastering

Samuel 2k Dec 29, 2022
[3DV 2021] Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimation

Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimation This is the official implementation for the method described in Ch

Jiaxing Yan 27 Dec 30, 2022
Seeing All the Angles: Learning Multiview Manipulation Policies for Contact-Rich Tasks from Demonstrations

Seeing All the Angles: Learning Multiview Manipulation Policies for Contact-Rich Tasks from Demonstrations Trevor Ablett, Daniel (Yifan) Zhai, Jonatha

STARS Laboratory 3 Feb 01, 2022
MutualGuide is a compact object detector specially designed for embedded devices

Introduction MutualGuide is a compact object detector specially designed for embedded devices. Comparing to existing detectors, this repo contains two

ZHANG Heng 103 Dec 13, 2022
The official PyTorch code for NeurIPS 2021 ML4AD Paper, "Does Thermal data make the detection systems more reliable?"

MultiModal-Collaborative (MMC) Learning Framework for integrating RGB and Thermal spectral modalities This is the official code for NeurIPS 2021 Machi

NeurAI 12 Nov 02, 2022
Template repository to build PyTorch projects from source on any version of PyTorch/CUDA/cuDNN.

The Ultimate PyTorch Source-Build Template Translations: 한국어 TL;DR PyTorch built from source can be x4 faster than a naïve PyTorch install. This repos

Joonhyung Lee/이준형 651 Dec 12, 2022
This is a simple plugin for Vim that allows you to use OpenAI Codex.

🤖 Vim Codex An AI plugin that does the work for you. This is a simple plugin for Vim that will allow you to use OpenAI Codex. To use this plugin you

Tom Dörr 195 Dec 28, 2022
STEM: An approach to Multi-source Domain Adaptation with Guarantees

STEM: An approach to Multi-source Domain Adaptation with Guarantees Introduction This is the official implementation of ``STEM: An approach to Multi-s

5 Dec 19, 2022
DirectVoxGO reconstructs a scene representation from a set of calibrated images capturing the scene.

DirectVoxGO reconstructs a scene representation from a set of calibrated images capturing the scene. We achieve NeRF-comparable novel-view synthesis quality with super-fast convergence.

sunset 709 Dec 31, 2022
Locally cache assets that are normally streamed in POPULATION: ONE

Population One Localizer This is no longer needed as of the build shipped on 03/03/22, thank you bigbox :) Locally cache assets that are normally stre

Ahman Woods 2 Mar 04, 2022
Official release of MSHT: Multi-stage Hybrid Transformer for the ROSE Image Analysis of Pancreatic Cancer axriv: http://arxiv.org/abs/2112.13513

MSHT: Multi-stage Hybrid Transformer for the ROSE Image Analysis This is the official page of the MSHT with its experimental script and records. We de

Tianyi Zhang 53 Dec 27, 2022
PanopticBEV - Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images

Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images This r

63 Dec 16, 2022