Etc/주식 자동 매매2022. 4. 23. 22:48
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### 캔들
 - 빨간색, 흰색 캔들: 상승
 - 파란색, 검정 캔들: 하락

1. 한 개의 캔들
1.1 상승샅바형캔들, Bullish Belt Hold Line (↑71%, ↓29%, +0.42)
1.2 하락샅바형캔들, Bearish Belt Hold Line (↑32%, ↓68%, -0.36)
1.3 망칭형, Hammer (↑60%, ↓40%, +0.2)
1.4 역망치 캔들, Inverted Hammer (↑35%, ↓65%, -0.3)
1.5 호리병 캔들, Takuri Line (↑66%, ↓34%, +0.32)

2. 두 개의 캔들
2.1 하락장악형 패턴, Bear Engulfing (↑21%, ↓79%, -0.58)
2.2 상승장악형 패턴, Bullish Engulfind (↑63%, ↓37%, +0.26)
2.3 상승지속형 패턴, Last Engulfing Top (↑68%, ↓32%, +0.36)
2.4 하락지속형 패턴, Last Engulfind Bottom (↑35%, ↓65%, -0.3)
2.5 상승형 십자캔들 별 패턴, Bullish Doji Star (↑36%, ↓64%, -0.28)
2.6 하락 십자캔들 별 패턴, Bearish Doji Star (↑69%, ↓31%, +0.38)
2.7 관통형 패턴, Piercing Line (↑64%, ↓36%, +0.28)
2.8 먹구름형패턴, Dark Cloud Cover (↑40%, ↓60%, -0.2)
2.9 중심선 위 상승 패턴, Above the Stomach (↑44%, ↓66%, -0.22)
2.10 중심선 아래 하락 패턴, Below the Stomach (↑40%, ↓60%, -0.2)
2.11 상승형 격자 패턴, Windows Rising (↑75%, ↓25%, +0.5)
2.12 하락형 격자 패턴, Windows Falling (↑33%, ↓67%, -0.34)
2.13 상승형 이별 패턴, Bullish Separating Lines (↑72%, ↓28%, +0.44)
2.14 하락형 이별 패턴, Bearish Separating Lines (↑37%, ↓63%, -0.26)

3. 세 개의 캔들
3.1 새벽별 패턴, Moring Star (↑78%, ↓22%, +0.56)
3.2 저녁별 패턴, Evening Star (↑28%, ↓72%, -0.44)
3.3 상승고아형, Bullish Abandoned Baby (↑70%, ↓30%, +0.4)
3.4 하락고아형, Bearish Abandoned Baby (↑31%, ↓69%, -0.38)
3.5 상승 막대샌드위치형 패턴, Bullish Stick Sandwich (↑38%, ↓62%, -0.24)
3.6 하락 막대샌드위치형 패턴, Bearish Stick Sandwich (↑62%, ↓38%, +0.24)
3.7 바닥 삼성형, Three Stars in the South (↑86%, ↓14%, +0.72)
3.8 천장 삼성형, Three Stars in the North (↑14%, ↓86%, -0.72)
3.9 적상병, Three White Soldiers (↑82%, ↓18%, +0.64)
3.10 흑삼병, Three Black Crows (↑22%, ↓78%, -0.56)
3.11 동일흑삼병, Identical Three Crows (↑21%, ↓79%, -0.58)
3.12 상승블록형, Advance Block (↑64%, ↓36%, +0.28)
3.13 지연형, Deliberation-Stalled Pattern (↑77%, ↓23%, +0.55)
3.14 갭상승 까마귀형, Upside Gap Two Crows (↑60%, ↓40%, +0.3)
3.15 하락장악확인형, Three Outside Down (↑31%, ↓69%, -0.38)
3.16 상승장악확인형, Three Outside Up (↑75%, ↓25%, +0.5)
3.17 상승잉태확인형, Three Inside Up (↑65%, ↓35%, +0.3)
3.18 폭발십자캔들팬턴, Collapsing Doji (↑37%, ↓63%, -0.26)
3.19 하락갭삼법, Downside Gap Three Methods (↑62%, ↓38%, +0.24)

 

end.

 

https://github.com/suparjotamin/stockie

 

GitHub - suparjotamin/stockie

Contribute to suparjotamin/stockie development by creating an account on GitHub.

github.com

 

https://towardsdatascience.com/how-to-identify-japanese-candlesticks-patterns-in-python-b835d1cc72f7

 

How to identify Japanese candlesticks patterns in Python

Japanese candlesticks are one of the most important tools for a discretionary or quantitative trader. They are the first example of a…

towardsdatascience.com

https://medium.com/analytics-vidhya/recognizing-over-50-candlestick-patterns-with-python-4f02a1822cb5

 

Recognizing over 50 Candlestick Patterns with Python

An easy to follow guide for leveraging candlestick patterns for ML

medium.com

 

 

 

https://www.skyer9.pe.kr/wordpress/?p=1773 

 

캔들 패턴 정리 – 상구리의 기술 블로그

캔들 패턴 정리 참조사이트 : https://www.feedroll.com/candlestick-patterns/1309-index-candlestick-patterns/ 벽돌형캔들(Marubozu) 은 위꼬리/아래꼬리가 없는 캔들을 의미합니다. 용어 정리 양봉(White)은 몸통 아래가

www.skyer9.pe.kr

 

 

1. 기본형태

https://tickkle.tistory.com/entry/%EC%BA%94%EB%93%A4%EA%B8%B0%EB%B3%B8%ED%98%95%ED%83%9C?category=1025295 

 

#13 캔들 차트 : 기본 형태

Long Days Long Days란 시가와 종가 사이의 차이, 즉 몸통이 이전 캔들 차트에 비해 상당히 큰 것을 말하며 주로 그림자보다 캔들의 몸통 길이가 큰 경이다. 하루 동안 시가와 종가의 차이가 크게나는

tickkle.tistory.com

2. 상승반전패턴

https://tickkle.tistory.com/entry/%EC%83%81%EC%8A%B9%EB%B0%98%EC%A0%84%ED%8C%A8%ED%84%B4?category=1025295 

 

#14 캔들 차트 : 상승 반전 패턴

역망치형 주가 하락 국면 중 하락 추세의 바닥에서 나타난 상승시도로써 종가가 거의 시초가 수준으로 마감하여 매우 짧은 몸통과 긴 위 그림자를 이루는 형태이다. 몸통의 색깔은 의미가 없고

tickkle.tistory.com

3. 상승지속패턴

https://tickkle.tistory.com/entry/%EC%83%81%EC%8A%B9%EC%A7%80%EC%86%8D%ED%8C%A8%ED%84%B4?category=1025295 

 

#15 캔들 차트 : 상승 지속 패턴

블록형 주가가 상승 추세를 이어가다 매수 탄력이 약화되어 주가 상승이 부담스러운 상황에서 나타나며 이틀전 캔들의 몸통에 비해 당일과 전일의 몸통이 짧고 위 그림자는 길게 형성되는 패턴

tickkle.tistory.com

4. 하락지속패턴

https://tickkle.tistory.com/entry/%ED%95%98%EB%9D%BD%EC%A7%80%EC%86%8D%ED%8C%A8%ED%84%B4?category=1025295 

 

#16 캔들 차트 : 하락 지속 패턴

하락 갭 타스키형 하락 추세가 이어지는 가운데 하락 갭을 형성하면서 음봉이 나타나고 그 뒤를이어 양봉이 발생한다. 이 양봉의 시가는 직전 음봉의 거래 범위에 있고 종가는 직전 음봉의 거래

tickkle.tistory.com

5. 하락반전패턴

https://tickkle.tistory.com/entry/%ED%95%98%EB%9D%BD%EB%B0%98%EC%A0%84%ED%8C%A8%ED%84%B4?category=1025295 

 

#17 캔들 차트 : 하락 반전 패턴

유성형 주가 상승 국면 중에 최고점에서 나타나며 시가가 갭을 이루며 상승 출발하였다가 긴 위 그림자를 혀성하며 추가 상승에 실패하며 되밀려서 시가 부근에서 종가가 형성되며 짧은 몸통을

tickkle.tistory.com

 

end.

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Posted by 하루y
Etc/주식 자동 매매2022. 4. 9. 20:25
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한국투자증권에 있는 지표 설명입니다.

시스템화 하면서 놓친 부분과 좀 더 고려해야 할 점에 대해서 주석을 추가했습니다.

 

1. 의미
1.1 전환선 및 기준선
 - 전환선이 기준선 위에 위치하에 되면 주가가 기준선 위에 있을 경우에는 기준선이 지지선이 됩니다.
1.2 후행스팬
 - 후행스팬이 26일전 주가를 상향 돌파하는 시점이 매수시점이 됩니다. 반대로 하향돌파하게 되면 매도 시점으로간주합니다.
1.3 선행스팬1, 선행스팬2 ==> 구름대: 
 - 선행스팬1과 선행스팬2와의 사이를 구름대라고 하며 주가가 구름대를 상향돌파하면매수시점, 하향돌파하면 매도시점이 됩니다. 또한 상승추세에서는 지지대역할을 하고,하향추세에서는 저항대 역할을 합니다.
 
2. 계산식
 주석: 각 식 모두 종가 기준으로 계산하고, 당일을 포함합니다.


 - 기준선 = (최근 26일간의 최고치 + 최저치) / 2
 - 전환선 = (최근 9일간의 최고치 + 최저치) / 2
 - 후행스팬 = 그날의 종가를 26일 후행시킨선
 - 단기선행스팬 = (기준선 + 전환선) / 2  를 26일 선행(앞으로) 시킨선
 - 장기선행스팬 = (최근 52일간의 최고치 + 최저치) / 2 를 26일 선행(앞으로)시킨선

3. 적용방법
3.1 기준선과 전환선을 이용한 분석 

 - 전환선이 기준선 위에 위치하면 매수시점으로, 전환선이 기준선 밑에 위치하면 매도시점으로 인식합니다. 이러한 기준으로매매시점을 파악할때는 기준선의 움직임을 관찰하여 기준선이 상승추세이면 매도를 보류하고, 기준선이 하락추세이면 매수를 보류하여야 whipsaw(속임수)를 줄일 수 있습니다. (주석: 기준선에 대한 추세 정보를 참조해야 매도, 매수를 정확성을 높일 수 있을 듯 함)

3.2 후행스팬을 이용한 분석
기준선이 상승추세를 유지하고 있을 때, 후행스팬이 주가를 상향돌파하면 강세장으로 전환될 확률이 높습니다. 이때 후행스팬이주가를 완전히 상향돌파하지 못하고 재차 하락세로 반전되면 시장은 강한 약세장이 지속될 확률이 높아집니다. 
후행스팬이 주가를 하향돌파하면 매도시점으로 인식하며, 하락하던 후행스팬이 주가를 완전히 하향돌파하지 못하고 다시 상승할경우 향후 시장은 더욱 강세장이 될 가능성이 높습니다. (주석: 보통은 전환선이 먼저 기준선을 하향 돌파함)

3.3 구름대를 이용한 분석
선행스팬1과 선행스팬2와의 사이를 구름대라고 하는데, 구름대는 상승국면에서는 지지구간의 역할을 하고 하락국면에서는저항구간의 역할을 합니다. 구름대의 두께는 지지나 저항세력의 강도와 밀접한 관계가 있습니다. 

(주석: 일목균형표 원점 참조. "26일 간의 중간값과 최근 52일간의 중간값이 큰 차가 없는 때야 말로 엄청나게 중요한 변화를 읽으키게 된다는 사실을 알게 될 것이다." => 구름대가 좁아진 시점이나 선행스팬1이 2를 전환하는 시점)

 

ps. 책에도 있자만 균형표에 적합한 종목을 찾아야 함. 어떻게? 이건 앞으로의 숙제.

 - "어떠한 주식이라도 이 균형표를 적용할 수 있으나 '자연스럽게 이 균형표에 잘 맞는 종목'을 발굴하여 접목하는 것이 무엇보다 중요하다." - 일목균형표1권

 

end.

 

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Posted by 하루y
Etc/주식 자동 매매2022. 3. 20. 20:04
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ImportError: DLL load failed: 지정된 프로시저를 찾을 수 없습니다.

 

갑자기 이런 메시가 나오면서 실행이 안됨.

 

1. anaconda prompt 해당 env에서 pyqt5 재설치

conda env list
conda activate system_tracking_py39_32     #<------ 3.9 / 32bit 환경 선택
pip uninstall pyqt5
pip install pyqt5

 

2. 만약 비슷한 오류나 가면서 안되며, open api도 재설치 한다.

 

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Posted by 하루y
Etc/주식 자동 매매2022. 3. 20. 17:53
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자료가 너무 많은 것도 고민... ㅎ...

 

참조주소: https://github.com/wilsonfreitas/awesome-quant#python

 

Python

Numerical Libraries & Data Structures

  • numpy - NumPy is the fundamental package for scientific computing with Python.
  • scipy - SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering.
  • pandas - pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
  • quantdsl - Domain specific language for quantitative analytics in finance and trading.
  • statistics - Builtin Python library for all basic statistical calculations.
  • sympy - SymPy is a Python library for symbolic mathematics.
  • pymc3 - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano.

Financial Instruments and Pricing

  • PyQL - QuantLib's Python port.
  • pyfin - Basic options pricing in Python. [ARCHIVED]
  • vollib - vollib is a python library for calculating option prices, implied volatility and greeks.
  • QuantPy - A framework for quantitative finance In python.
  • Finance-Python - Python tools for Finance.
  • ffn - A financial function library for Python.
  • pynance - Lightweight Python library for assembling and analysing financial data.
  • tia - Toolkit for integration and analysis.
  • hasura/base-python-dash - Hasura quickstart to deploy Dash framework. Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python.
  • hasura/base-python-bokeh - Hasura quickstart to visualize data with bokeh library.
  • pysabr - SABR model Python implementation.
  • FinancePy - A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.
  • gs-quant - Python toolkit for quantitative finance
  • willowtree - Robust and flexible Python implementation of the willow tree lattice for derivatives pricing.
  • financial-engineering - Applications of Monte Carlo methods to financial engineering projects, in Python.
  • optlib - A library for financial options pricing written in Python.
  • tf-quant-finance - High-performance TensorFlow library for quantitative finance.
  • finoptions - Complete python implementation of R package fOptions with partial implementation of fExoticOptions for pricing various options.

Indicators

Trading & Backtesting

  • Blankly - Fully integrated backtesting, paper trading, and live deployment.
  • TA-Lib - perform technical analysis of financial market data.
  • trade - trade is a Python framework for the development of financial applications.
  • zipline - Pythonic algorithmic trading library.
  • QuantSoftware Toolkit - Python-based open source software framework designed to support portfolio construction and management.
  • quantitative - Quantitative finance, and backtesting library.
  • analyzer - Python framework for real-time financial and backtesting trading strategies.
  • bt - Flexible Backtesting for Python.
  • backtrader - Python Backtesting library for trading strategies.
  • pythalesians - Python library to backtest trading strategies, plot charts, seamlessly download market data, analyse market patterns etc.
  • pybacktest - Vectorized backtesting framework in Python / pandas, designed to make your backtesting easier.
  • pyalgotrade - Python Algorithmic Trading Library.
  • tradingWithPython - A collection of functions and classes for Quantitative trading.
  • Pandas TA - Pandas TA is an easy to use Python 3 Pandas Extension with 115+ Indicators. Easily build Custom Strategies.
  • ta - Technical Analysis Library using Pandas (Python)
  • algobroker - This is an execution engine for algo trading.
  • pysentosa - Python API for sentosa trading system.
  • finmarketpy - Python library for backtesting trading strategies and analyzing financial markets.
  • binary-martingale - Computer program to automatically trade binary options martingale style.
  • fooltrader - the project using big-data technology to provide an uniform way to analyze the whole market.
  • zvt - the project using sql,pandas to provide an uniform and extendable way to record data,computing factors,select securites, backtesting,realtime trading and it could show all of them in clearly charts in realtime.
  • pylivetrader - zipline-compatible live trading library.
  • pipeline-live - zipline's pipeline capability with IEX for live trading.
  • zipline-extensions - Zipline extensions and adapters for QuantRocket.
  • moonshot - Vectorized backtester and trading engine for QuantRocket based on Pandas.
  • PyPortfolioOpt - Financial portfolio optimisation in python, including classical efficient frontier and advanced methods.
  • Eiten - Eiten is an open source toolkit by Tradytics that implements various statistical and algorithmic investing strategies such as Eigen Portfolios, Minimum Variance Portfolios, Maximum Sharpe Ratio Portfolios, and Genetic Algorithms based Portfolios.
  • riskparity.py - fast and scalable design of risk parity portfolios with TensorFlow 2.0
  • mlfinlab - Implementations regarding "Advances in Financial Machine Learning" by Marcos Lopez de Prado. (Feature Engineering, Financial Data Structures, Meta-Labeling)
  • pyqstrat - A fast, extensible, transparent python library for backtesting quantitative strategies.
  • NowTrade - Python library for backtesting technical/mechanical strategies in the stock and currency markets.
  • pinkfish - A backtester and spreadsheet library for security analysis.
  • aat - Async Algorithmic Trading Engine
  • Backtesting.py - Backtest trading strategies in Python
  • catalyst - An Algorithmic Trading Library for Crypto-Assets in Python
  • quantstats - Portfolio analytics for quants, written in Python
  • qtpylib - QTPyLib, Pythonic Algorithmic Trading http://qtpylib.io
  • Quantdom - Python-based framework for backtesting trading strategies & analyzing financial markets [GUI]
  • freqtrade - Free, open source crypto trading bot
  • algorithmic-trading-with-python - Free pandas and scikit-learn resources for trading simulation, backtesting, and machine learning on financial data.
  • DeepDow - Portfolio optimization with deep learning
  • Qlib - An AI-oriented Quantitative Investment Platform by Microsoft. Full ML pipeline of data processing, model training, back-testing; and covers the entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution.
  • machine-learning-for-trading - Code and resources for Machine Learning for Algorithmic Trading
  • AlphaPy - Automated Machine Learning [AutoML] with Python, scikit-learn, Keras, XGBoost, LightGBM, and CatBoost
  • jesse - An advanced crypto trading bot written in Python
  • rqalpha - A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities.
  • FinRL-Library - A Deep Reinforcement Learning Library for Automated Trading in Quantitative Finance. NeurIPS 2020.
  • bulbea - Deep Learning based Python Library for Stock Market Prediction and Modelling.
  • ib_nope - Automated trading system for NOPE strategy over IBKR TWS.
  • OctoBot - Open source cryptocurrency trading bot for high frequency, arbitrage, TA and social trading with an advanced web interface.
  • bta-lib - Technical Analysis library in pandas for backtesting algotrading and quantitative analysis.
  • Stock-Prediction-Models - Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations.
  • tda-api - Gather data and trade equities, options, and ETFs via TDAmeritrade.
  • vectorbt - Find your trading edge, using a powerful toolkit for backtesting, algorithmic trading, and research.
  • Lean - Lean Algorithmic Trading Engine by QuantConnect (Python, C#).
  • fast-trade - Low code backtesting library utilizing pandas and technical analysis indicators.

Risk Analysis

  • pyfolio - Portfolio and risk analytics in Python.
  • empyrical - Common financial risk and performance metrics.
  • fecon235 - Computational tools for financial economics include: Gaussian Mixture model of leptokurtotic risk, adaptive Boltzmann portfolios.
  • finance - Financial Risk Calculations. Optimized for ease of use through class construction and operator overload.
  • qfrm - Quantitative Financial Risk Management: awesome OOP tools for measuring, managing and visualizing risk of financial instruments and portfolios.
  • visualize-wealth - Portfolio construction and quantitative analysis.
  • VisualPortfolio - This tool is used to visualize the perfomance of a portfolio.
  • universal-portfolios - Collection of algorithms for online portfolio selection.
  • FinQuant - A program for financial portfolio management, analysis and optimisation.
  • Empyrial - Portfolio's risk and performance analytics and returns predictions.
  • risktools - Risk tools for use within the crude and crude products trading space with partial implementation of R's PerformanceAnalytics.
  • Riskfolio-Lib - Portfolio Optimization and Quantitative Strategic Asset Allocation in Python.

Factor Analysis

  • alphalens - Performance analysis of predictive alpha factors.
  • Spectre - GPU-accelerated Factors analysis library and Backtester

Time Series

  • ARCH - ARCH models in Python.
  • statsmodels - Python module that allows users to explore data, estimate statistical models, and perform statistical tests.
  • dynts - Python package for timeseries analysis and manipulation.
  • PyFlux - Python library for timeseries modelling and inference (frequentist and Bayesian) on models.
  • tsfresh - Automatic extraction of relevant features from time series.
  • hasura/quandl-metabase - Hasura quickstart to visualize Quandl's timeseries datasets with Metabase.
  • Facebook Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
  • tsmoothie - A python library for time-series smoothing and outlier detection in a vectorized way.
  • pmdarima - A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

Calendars

Data Sources

  • yfinance - Yahoo! Finance market data downloader (+faster Pandas Datareader)
  • findatapy - Python library to download market data via Bloomberg, Quandl, Yahoo etc.
  • googlefinance - Python module to get real-time stock data from Google Finance API.
  • yahoo-finance - Python module to get stock data from Yahoo! Finance.
  • pandas-datareader - Python module to get data from various sources (Google Finance, Yahoo Finance, FRED, OECD, Fama/French, World Bank, Eurostat...) into Pandas datastructures such as DataFrame, Panel with a caching mechanism.
  • pandas-finance - High level API for access to and analysis of financial data.
  • pyhoofinance - Rapidly queries Yahoo Finance for multiple tickers and returns typed data for analysis.
  • yfinanceapi - Finance API for Python.
  • yql-finance - yql-finance is simple and fast. API returns stock closing prices for current period of time and current stock ticker (i.e. APPL, GOOGL).
  • ystockquote - Retrieve stock quote data from Yahoo Finance.
  • wallstreet - Real time stock and option data.
  • stock_extractor - General Purpose Stock Extractors from Online Resources.
  • Stockex - Python wrapper for Yahoo! Finance API.
  • finsymbols - Obtains stock symbols and relating information for SP500, AMEX, NYSE, and NASDAQ.
  • FRB - Python Client for FRED® API.
  • inquisitor - Python Interface to Econdb.com API.
  • yfi - Yahoo! YQL library.
  • chinesestockapi - Python API to get Chinese stock price.
  • exchange - Get current exchange rate.
  • ticks - Simple command line tool to get stock ticker data.
  • pybbg - Python interface to Bloomberg COM APIs.
  • ccy - Python module for currencies.
  • tushare - A utility for crawling historical and Real-time Quotes data of China stocks.
  • jsm - Get the japanese stock market data.
  • cn_stock_src - Utility for retrieving basic China stock data from different sources.
  • coinmarketcap - Python API for coinmarketcap.
  • after-hours - Obtain pre market and after hours stock prices for a given symbol.
  • bronto-python - Bronto API Integration for Python.
  • pytdx - Python Interface for retrieving chinese stock realtime quote data from TongDaXin Nodes.
  • pdblp - A simple interface to integrate pandas and the Bloomberg Open API.
  • tiingo - Python interface for daily composite prices/OHLC/Volume + Real-time News Feeds, powered by the Tiingo Data Platform.
  • iexfinance - Python Interface for retrieving real-time and historical prices and equities data from The Investor's Exchange.
  • pyEX - Python interface to IEX with emphasis on pandas, support for streaming data, premium data, points data (economic, rates, commodities), and technical indicators.
  • alpaca-trade-api - Python interface for retrieving real-time and historical prices from Alpaca API as well as trade execution.
  • metatrader5 - API Connector to MetaTrader 5 Terminal
  • akshare - AkShare is an elegant and simple financial data interface library for Python, built for human beings! https://akshare.readthedocs.io
  • yahooquery - Python interface for retrieving data through unofficial Yahoo Finance API.
  • investpy - Financial Data Extraction from Investing.com with Python! https://investpy.readthedocs.io/
  • yliveticker - Live stream of market data from Yahoo Finance websocket.
  • bbgbridge - Easy to use Bloomberg Desktop API wrapper for Python.
  • alpha_vantage - A python wrapper for Alpha Vantage API for financial data.
  • FinanceDataReader - Open Source Financial data reader for U.S, Korean, Japanese, Chinese, Vietnamese Stocks
  • pystlouisfed - Python client for Federal Reserve Bank of St. Louis API - FRED, ALFRED, GeoFRED and FRASER

Excel Integration

  • xlwings - Make Excel fly with Python.
  • openpyxl - Read/Write Excel 2007 xlsx/xlsm files.
  • xlrd - Library for developers to extract data from Microsoft Excel spreadsheet files.
  • xlsxwriter - Write files in the Excel 2007+ XLSX file format.
  • xlwt - Library to create spreadsheet files compatible with MS Excel 97/2000/XP/2003 XLS files, on any platform.
  • DataNitro - DataNitro also offers full-featured Python-Excel integration, including UDFs. Trial downloads are available, but users must purchase a license.
  • xlloop - XLLoop is an open source framework for implementing Excel user-defined functions (UDFs) on a centralised server (a function server).
  • expy - The ExPy add-in allows easy use of Python directly from within an Microsoft Excel spreadsheet, both to execute arbitrary code and to define new Excel functions.
  • pyxll - PyXLL is an Excel add-in that enables you to extend Excel using nothing but Python code.

Visualization

  • D-Tale - Visualizer for pandas dataframes and xarray datasets.
  • mplfinance - matplotlib utilities for the visualization, and visual analysis, of financial data.
  • finplot - Performant and effortless finance plotting for Python.
  • finvizfinance - Finviz analysis python library.
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Posted by 하루y