Python量化金融都需要用到哪些库？最全汇总-技术圈

大家好，我是老表

今天分享一篇Python量化金融最全汇总，推荐大家收藏～记得划到文末点赞呐～

本文汇总了定量金融的大量三方库，按功能进行分类，覆盖数值运算，衍生品定价，回溯检验，风险管理，数据爬取，可视化等多个子领域，供每个Python程序员参考。

不要重复造轮子，明确要解决的问题，然后寻找相应的工具。很多著名的包如Numpy，Pandas，Seaborn，backtrader等已经被证明高度有效，即便没有找到符合应用场景的包，类似的工具也能够为创建自己的解决方案提供参考。

内容来源于Github项目《Awesome Quant》，由Wilson Freitas创作，项目链接：Awesome Quant^[1]

科学运算和数据结构

numpy^[2] - 进行数值运算的基础包，scipy和numpy令Python进行有效的矩阵运算成为可能
scipy^[3] - 科学计算生态系统，广泛应用于数学，物理学和工程学等自然科学领域
pandas^[4] - 提供了高性能的数据结构和数据分析工具
quantdsl^[5] - 金融/交易领域进行定量分析的领域特定语言
statistics^[6] - 进行基础统计运算
sympy^[7] - 专门用于符号数学
pymc3^[8] - 用Python实现概率编程，贝叶斯建模，用Theano实现概率机器学习

金融工具和定价

PyQL^[9] - Quantlib的Python接口
pyfin^[10] - 期权定价
vollib^[11] - 计算期权价格，隐含波动率和希腊值
QuantPy^[12] - 定量金融分析
Finance-Python^[13] - 定量金融分析
ffn^[14] - 拓展Pandas，提供一系列函数进行基础的量化分析
pynance^[15] - 获取股票和衍生品市场的数据，分析和可视化
hasura/base-python-dash^[16] - 快速入门部署Dash应用，Dash基于Flask，Plotly.js和React.js，允许用户用纯Python快速搭建强大的数据科学网页App
hasura/base-python-bokeh^[17] - 如何用Bokeh实现数据可视化
pysabr^[18] - 用Python实现SABR模型

技术指标

pandas_talib^[19] - 整合Pandas和Talib，用pandas计算技术指标
finta^[20] - 用Pandas计算常见的技术指标
Tulipy^[21] - 技术指标库（tulipindicators的Python绑定）

量化交易/回溯检验

TA-Lib^[22] - 计算技术指标，跟Numpy深度整合
trade^[23] - 用于开发金融应用的基础包
zipline^[24] - 强大的回溯检验框架，被很多量化交易平台作为底层技术，包括Qauntopian, 聚宽等
QuantSoftware Toolkit^[25] - 创建和管理投资组合
quantitative^[26] - 定量金融的基础工具，回溯检验
analyzer^[27] - 接收实时报价并回溯检验
bt^[28] - 回溯检验框架，比Zipline更灵活
backtrader^[29] - 回溯检验框架，支持实盘交易，过去几年快速崛起，已成为最流行的量化工具之一
pythalesians^[30] - 回溯检验框架
pybacktest^[31] - 向量化回溯检验框架，向量化允许进行快速的回溯，但检验精度不高
pyalgotrade^[32] - 回溯检验框架
tradingWithPython^[33] - 提供一系列函数和自定义类来管理量化交易
Pandas TA^[34] - 拓展Pandas，包含115种技术指标，快速创建交易策略
ta^[35] - 用Pandas计算技术指标
algobroker^[36] - 算法交易的部署引擎
pysentosa^[37] - sentosa交易系统的Python接口
finmarketpy^[38] - 分析市场数据，支持简单回溯检验
binary-martingale^[39] - 自动化交易程序，用马丁格尔策略交易二元期权
fooltrader^[40] - 利用大数据技术进行量化分析，包含回溯检验
zvt^[41] - 提供统一和灵活的方式来获取数据，计算因子，选股，回溯检验和实盘交易
pylivetrader^[42] - 兼容zipline的实时交易库
pipeline-live^[43] - zipline扩展库，用于实盘交易
zipline-extensions^[44] - Zipline扩展，适配QuantRocket
moonshot^[45] - 向量化回溯检验和交易引擎
PyPortfolioOpt^[46] - 金融投资组合优化，包括创建有效边界和其它高级算法
riskparity.py^[47] - 用TensorFlow设计风险平价投资组合
mlfinlab^[48] - 《金融机器学习应用》一书的实现
pyqstrat^[49] - 快速地回测交易策略
pinkfish^[50] - 证券分析
aat^[51] - 异步算法交易引擎
Backtesting.py^[52] - 回溯检验框架
catalyst^[53] - 回溯检验框架，专门用于数字货币市场
quantstats^[54] - 投资组合分析
qtpylib^[55] - 回溯检验框架，支持实盘交易
freqtrade^[56] - 开源数字货币交易机器人
algorithmic-trading-with-python^[57] - 《Python算法交易》一书的源码和数据
DeepDow^[58] - 用深度学习优化投资组合

风险分析

pyfolio^[59] - 计算投资组合和交易策略的业绩指标
empyrical^[60] - 计算常用的风险和业绩指标
fecon235^[61] - 金融计量经济工具包，包括leptokurtotic风险高斯混合模型，自适应Boltzmann投资组合
finance^[62] - 计算金融风险
qfrm^[63] - 定量金融风险管理
visualize-wealth^[64] - 构建投资组合和定量分析
VisualPortfolio^[65] - 可视化投资组合表现

因子分析

alphalens^[66] - 分析预测性因子的表现

时间序列

ARCH^[67] - Python实现ARCH模型
statsmodels^[68] - 计量经济模型库，用于创建回归模型，统计检验，时序模型
dynts^[69] - 操纵和分析时间序列
PyFlux^[70] - 时间序列模型和因果推断
tsfresh^[71] - 从时间序列中提取有意义的特征
hasura/quandl-metabase^[72] - 可视化Quandl的时间序列数据集

日历

trading_calendars^[73] - 股票交易所财经日历
bizdays^[74] - 工作日计算和效用工具
pandas_market_calendars^[75] - 拓展Pandas，股票交易所财经日历

数据源

findatapy^[76] - 获取彭博终端，Quandl和雅虎财经的数据
googlefinance^[77] - 从谷歌财经获取实时股票价格
yahoo-finance^[78] - 从雅虎财经下载股票报价，历史价格，产品信息和财务报表
pandas-datareader^[79] - 从多个数据源获取经济/金融时间序列，包括谷歌财经，雅虎财经，圣路易斯联储(FRED)，OECD, Fama/French，世界银行，欧元区统计局等，是Pandas生态系统的重要组成
pandas-finance^[80] - 提供高级接口下载和分析金融时间序列
pyhoofinance^[81] - 从雅虎财经批量获取股票数据
yfinanceapi^[82] - 从雅虎财经获取数据
yql-finance^[83] - 从雅虎财经获取数据
ystockquote^[84] - 从雅虎财经获取实时报价
wallstreet^[85] - 实时股票和期权报价
stock_extractor^[86] - 从网络上爬取股票信息
Stockex^[87] - 从雅虎财经获取数据
finsymbols^[88] - 获取全美证券交易所，纽约证券交易所和纳斯达克上市公司的详细数据
inquisitor^[89] - 从Econdb获取经济数据，Econdb是全球经济指标聚合器
chinesestockapi^[90] - 获取A股数据
exchange^[91] - 获取最新的汇率报价
ticks^[92] - 命令行程序，获取股票报价
pybbg^[93] - 彭博终端COM的Python接口
ccy^[94] - 获取外汇数据
tushare^[95] - 获取中国股票，基金，债券和期货市场的历史数据
jsm^[96] - 获取日本股票市场的历史数据
cn_stock_src^[97] - 从不同数据源获取中国的股票数据
coinmarketcap^[98] - 从coinmarketcap获取数字货币数据
after-hours^[99] - 获取美股盘前和盘后的市场价格
bronto-python^[100] - 整合Bronto API接
pytdx^[101] - 获取中国国内股票的实时报价
pdblp^[102] - 整合Pandas和彭博终端的公共接口
tiingo^[103] - 从Tiingo平台获取股票日K线和实时报价/新闻流
IEX^[104] - 从IEX交易所获取股票的实时报价和历史数据
alpaca-trade-api^[105] - 从Alpaca平台获取股票实时报价和历史数据，并提供交易接口交易美股
metatrader5^[106] - 集成Python和MQL5交易平台，适合外汇交易
akshare^[107] - 获取中国股票，基金，债券和宏观经济数据
yahooquery^[108] - 从雅虎财经获取数据
investpy^[109] - 从英为财经(Investing.com)获取数据
yliveticker^[110] - 从雅虎财经通过Websocket获取实时报价

Excel集成

xlwings^[111] - 深度整合Python和Excel
openpyxl^[112] - 读取/写入Excel 2007 xlsx/xlsm文件
xlrd^[113] - 从Excel电子表格提取数据
xlsxwriter^[114] - 将数据写入Excel电子表格
xlwt^[115] - 创建跨平台和向后兼容的电子表格
DataNitro^[116] - 深度整合Python和Excel，可免费试用，商业付费软件
xlloop^[117] - 创建Excel用户自定义函数
expy^[118] - Excel插件，允许用户从电子表格中执行Python代码和定义自定义函数
pyxll^[119] - Excel插件，从Excel中执行Python代码

可视化

Matplotlib^[120] - Python数据可视化的基础包，从二维图表到三维图表
Seaborn^[121] - 基于Matplotlib，快速创建美观的统计图表
Plotly^[122] - 创建动态和交互式的图表
Altair^[123] - 统计可视化工具，同时支持静态和交互式图表
D-Tale^[124] - 可视化Pandas数据结构

地址链接

[1]

Awesome Quant: https//github.com/wilsonfreitas/awesome-quant

[2]

numpy: https//www.numpy.org/

[3]

scipy: https//www.scipy.org/

[4]

pandas: https//pandas.pydata.org/

[5]

quantdsl: https//github.com/johnbywater/quantdsl

[6]

statistics: https//docs.python.org/3/library/statistics.html

[7]

sympy: https//www.sympy.org/

[8]

pymc3: https//docs.pymc.io/

[9]

PyQL: https//github.com/enthought/pyql

[10]

pyfin: https//github.com/opendoor-labs/pyfin

[11]

vollib: https//github.com/vollib/vollib

[12]

QuantPy: https//github.com/jsmidt/QuantPy

[13]

Finance-Python: https//github.com/alpha-miner/Finance-Python

[14]

ffn: https//github.com/pmorissette/ffn

[15]

pynance: https//pynance.net/

[16]

hasura/base-python-dash: https//platform.hasura.io/hub/projects/hasura/base-python-dash

[17]

hasura/base-python-bokeh: https//platform.hasura.io/hub/projects/hasura/base-python-bokeh

[18]

pysabr: https//github.com/ynouri/pysabr

[19]

pandas_talib: https//github.com/femtotrader/pandas_talib

[20]

finta: https//github.com/peerchemist/finta

[21]

Tulipy: https//github.com/cirla/tulipy

[22]

TA-Lib: https//ta-lib.org/

[23]

trade: https//github.com/rochars/trade

[24]

zipline: https//www.zipline.io/

[25]

QuantSoftware Toolkit: https//github.com/QuantSoftware/QuantSoftwareToolkit

[26]

quantitative: https//github.com/jeffrey-liang/quantitative

[27]

analyzer: https//github.com/llazzaro/analyzer

[28]

bt: https//github.com/pmorissette/bt

[29]

backtrader: https//github.com/backtrader/backtrader

[30]

pythalesians: https//github.com/thalesians/pythalesians

[31]

pybacktest: https//github.com/ematvey/pybacktest

[32]

pyalgotrade: https//github.com/gbeced/pyalgotrade

[33]

tradingWithPython: https//pypi.org/project/tradingWithPython/

[34]

Pandas TA: https//github.com/twopirllc/pandas-ta

[35]

ta: https//github.com/bukosabino/ta

[36]

algobroker: https//github.com/joequant/algobroker

[37]

pysentosa: https//pypi.org/project/pysentosa/

[38]

finmarketpy: https//github.com/cuemacro/finmarketpy

[39]

binary-martingale: https//github.com/metaperl/binary-martingale

[40]

fooltrader: https//github.com/foolcage/fooltrader

[41]

zvt: https//github.com/zvtvz/zvt

[42]

pylivetrader: https//github.com/alpacahq/pylivetrader

[43]

pipeline-live: https//github.com/alpacahq/pipeline-live

[44]

zipline-extensions: https//github.com/quantrocket-llc/zipline-extensions

[45]

moonshot: https//github.com/quantrocket-llc/moonshot

[46]

PyPortfolioOpt: https//github.com/robertmartin8/PyPortfolioOpt

[47]

riskparity.py: https//github.com/dppalomar/riskparity.py

[48]

mlfinlab: https//github.com/hudson-and-thames/mlfinlab

[49]

pyqstrat: https//github.com/abbass2/pyqstrat

[50]

pinkfish: https//github.com/fja05680/pinkfish

[51]

aat: https//github.com/timkpaine/aat

[52]

Backtesting.py: https//kernc.github.io/backtesting.py/

[53]

catalyst: https//github.com/enigmampc/catalyst

[54]

quantstats: https//github.com/ranaroussi/quantstats

[55]

qtpylib: https//github.com/ranaroussi/qtpylib

[56]

freqtrade: https//github.com/freqtrade/freqtrade

[57]

algorithmic-trading-with-python: https//github.com/chrisconlan/algorithmic-trading-with-python

[58]

DeepDow: https//github.com/jankrepl/deepdow

[59]

pyfolio: https//github.com/quantopian/pyfolio

[60]

empyrical: https//github.com/quantopian/empyrical

[61]

fecon235: https//github.com/rsvp/fecon235

[62]

finance: https//pypi.org/project/finance/

[63]

qfrm: https//pypi.org/project/qfrm/

[64]

visualize-wealth: https//github.com/benjaminmgross/visualize-wealth

[65]

VisualPortfolio: https//github.com/wegamekinglc/VisualPortfolio

[66]

alphalens: https//github.com/quantopian/alphalens

[67]

ARCH: https//github.com/bashtage/arch

[68]

statsmodels: https://link.zhihu.com/?target=http%3A//statsmodels.sourceforge.net/

[69]

dynts: https//github.com/quantmind/dynts

[70]

PyFlux: https//github.com/RJT1990/pyflux

[71]

tsfresh: https//github.com/blue-yonder/tsfresh

[72]

hasura/quandl-metabase: https//platform.hasura.io/hub/projects/anirudhm/quandl-metabase-time-series

[73]

trading_calendars: https//github.com/quantopian/trading_calendars

[74]

bizdays: https//github.com/wilsonfreitas/python-bizdays

[75]

pandas_market_calendars: https//github.com/rsheftel/pandas_market_calendars

[76]

findatapy: https//github.com/cuemacro/findatapy

[77]

googlefinance: https//github.com/hongtaocai/googlefinance

[78]

yahoo-finance: https//github.com/lukaszbanasiak/yahoo-finance

[79]

pandas-datareader: https//github.com/pydata/pandas-datareader

[80]

pandas-finance: https//github.com/davidastephens/pandas-finance

[81]

pyhoofinance: https//github.com/innes213/pyhoofinance

[82]

yfinanceapi: https//github.com/Karthik005/yfinanceapi

[83]

yql-finance: https//github.com/slawek87/yql-finance

[84]

ystockquote: https//github.com/cgoldberg/ystockquote

[85]

wallstreet: https//github.com/mcdallas/wallstreet

[86]

stock_extractor: https//github.com/ZachLiuGIS/stock_extractor

[87]

Stockex: https//github.com/cttn/Stockex

[88]

finsymbols: https//github.com/skillachie/finsymbols

[89]

inquisitor: https//github.com/econdb/inquisitor

[90]

chinesestockapi: https//pypi.org/project/chinesestockapi/

[91]

exchange: https//github.com/akarat/exchange

[92]

ticks: https//github.com/jamescnowell/ticks

[93]

pybbg: https//github.com/bpsmith/pybbg

[94]

ccy: https//github.com/lsbardel/ccy

[95]

tushare: https//pypi.org/project/tushare/

[96]

jsm: https//pypi.org/project/jsm/

[97]

cn_stock_src: https//github.com/jealous/cn_stock_src

[98]

coinmarketcap: https//github.com/barnumbirr/coinmarketcap

[99]

after-hours: https//github.com/datawrestler/after-hours

[100]

bronto-python: https//pypi.org/project/bronto-python/

[101]

pytdx: https//github.com/rainx/pytdx

[102]

pdblp: https//github.com/matthewgilbert/pdblp

[103]

tiingo: https//github.com/hydrosquall/tiingo-python

[104]

IEX: https//github.com/addisonlynch/iexfinance

[105]

alpaca-trade-api: https//github.com/alpacahq/alpaca-trade-api-python

[106]

metatrader5: https//pypi.org/project/MetaTrader5/

[107]

akshare: https//github.com/jindaxiang/akshare

[108]

yahooquery: https//github.com/dpguthrie/yahooquery

[109]

investpy: https//github.com/alvarobartt/investpy

[110]

yliveticker: https//github.com/yahoofinancelive/yliveticker

[111]

xlwings: https//www.xlwings.org/

[112]

openpyxl: https//openpyxl.readthedocs.io/en/latest/

[113]

xlrd: https//github.com/python-excel/xlrd

[114]

xlsxwriter: https//xlsxwriter.readthedocs.io/

[115]

xlwt: https//github.com/python-excel/xlwt

[116]

DataNitro: https//datanitro.com/

[117]

xlloop: https://link.zhihu.com/?target=http%3A//xlloop.sourceforge.net/

[118]

expy: https://link.zhihu.com/?target=http%3A//www.bnikolic.co.uk/expy/expy.html

[119]

pyxll: https//www.pyxll.com/

[120]

Matplotlib: https//matplotlib.org/tutorials/index.html

[121]

Seaborn: https//seaborn.pydata.org/

[122]

Plotly: https//plotly.com/python/

[123]

Altair: https//altair-viz.github.io/index.html

[124]

D-Tale: https//github.com/man-group/dtale

来源：网络
编辑：【数据STUDIO】云朵君
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