Open Source Python Financial Software

Pyspread is a non-traditional spreadsheet application that is based on and written in the programming language Python. The goal of pyspread is to be the most pythonic spreadsheet. Pyspread expects Python expressions in its grid cells, which makes a spreadsheet specific language obsolete. Each cell returns a Python object that can be accessed from other cells. These objects can represent anything including lists or matrices. Dependencies + Python (>=2.7, <3.0) + numpy (>=1.1.0) + wxPython (>=2.8.10.1, Unicode version required) + matplotlib (>=1.1.1) + pycairo (>=1.8.8) Optional dependencies + python-gnupg (>=0.3.0, for opening own files without approval) + xlrd (>=0.9.2, for loading Excel® files) + xlwt (>=0.9.2, for saving Excel files, pyspread >=v0.3.0 required) + jedi (>=0.8.0, for tab completion and context help in the entry line, pyspread >=v0.3.0 required) + basemap (>=1.0.7, for the weather example pys file)

The Financial Data Access Library provides an abstraction of market data sources. It verifies data consistency and provides transformation to different time frame e.g. daily to weely. Includes SQL, CSI, ASCII and online data sources such as Yahoo!

Mini ERP, accounting and warehousing solution

NovaGrid is a light spreadsheet made with Python / Tkinter, based on Tktable. The objective of this project is to have a light spreasheet python object which can be used into larger projects. The code is totaly written in Python (actualy 2.2.2).

Ocular is a spreadsheet written entirely in python. Cell contents are evaluated by python after any standard spreadsheet coordinates are parsed. This allows the full Monty from Python to be implemented in a visual environment.

The main purpose of "python2xlw" is to create an Excel-compatible file which can be sent to users via the web as an excel application. The motivation is mainly to support the display of XY Scatter plots and tabular numerical data(eg engineering data)

Python tools to harvest financial data supplied by Yahoo Finance.

Abu Quantitative Integrated AI Big Data System, K-Line Pattern System, Classic Indicator System, Trend Analysis System, Time Series Dimension System, Statistical Probability System, and Traditional Moving Average System conduct in-depth quantitative analysis of investment varieties, completely crossing the user's complex code quantification stage, more suitable for ordinary people to use, towards the era of vectorization 2.0. The above system combines hundreds of seed quantitative models, such as financial time series loss model, deep pattern quality assessment model, long and short pattern combination evaluation model, long pattern stop-loss strategy model, short pattern covering strategy model, big data K-line pattern Historical portfolio fitting model, trading position mentality model, dopamine quantification model, inertial residual resistance support model, long-short swap revenge probability model, strong and weak confrontation model, trend angle change rate model, etc.