finmarketpy is a Python based library that enables you to analyze market data and also to backtest trading strategies using a simple to use API, which has prebuilt templates for you to define backtest. Included in the library

• Prebuilt templates for backtesting trading strategies
• Display historical returns for trading strategies
• Investigate seasonality of trading strategies
• Conduct market event studies around data events
• In built calculator for risk weighting using volatility targeting
• Written in object oriented way to make code more reusable

I had previously written the open source PyThalesians financial library (which has been merged with this - so can focus on maintaining one set of libraries). This new finmarketpy library has

• Similar functionality to the trading part of pythalesians
• Rewritten the API to make it much cleaner and easier to use, as well as having many new features.
• finmarketpy requires the libraries, which I've written chartpy (for charts) and findatapy (for loading market data) to function
• By splitting up into smaller more specialised libraries, it should make it easier for contributors
• Using findatapy, you can download market data easily from Bloomberg, Quandl, Yahoo etc
• Using chartpy, you can choose to have results displayed in matplotlib, plotly or bokeh by changing single keyword!

Points to note:

• Please bear in mind at present finmarketpy is currently a highly experimental alpha project and isn't yet fully documented
• Uses Apache 2.0 licence

Calculate the cumulative returns of a trading strategy historically (see examples/tradingmodelfxtrend_example.py)

Plot the leverage of the strategy over time

Plot the individual trade returns

Calculate seasonality of any asset: here we show gold and FX volatility seasonality (see examples/seasonality_examples.py)

Calculate event study around events for asset (see examples/events_examples.py)

Major requirements

• Required: Python 3.4, 3.5
• Required: pandas 0.18, numpy etc.
• Required: findatapy for downloading market data (https://github.com/cuemacro/findatapy)
• Required: chartpy for funky interactive plots (https://github.com/cuemacro/chartpy)
• Recommended: multiprocessor_on_dill because standard multiprocessing library pickle causes issues (from https://github.com/sixty-north/multiprocessing_on_dill)

You can install the library using the below (better to get the newest version from repo, as opposed to releases). After installation:

• Make sure you edit the MarketConstants file

pip install git+https://github.com/cuemacro/finmarketpy.git

But beforehand please make sure you have already installed both chartpy, findatapy and any other dependencies

pip install git+https://github.com/cuemacro/chartpy.git
pip install git+https://github.com/cuemacro/findatapy.git

In finmarketpy/examples you will find several examples, including some simple trading models

• 0.11 - finmarketpy
• First prerelease version

• 17 Nov 2016 - Changed source to ChartConstants default for TradingModel
• 14 Oct 2016 - Fixed arctic references in MarketConstants
• 13 Oct 2016 - Fixed IR plotting for BacktestEngine, added YoY metric plots
• 11 Oct 2016 - Added to TradeAnalysis another way to plot return statistics for a portfolio
• 10 Oct 2016 - Added returns_example to show how to use PyFolio via finmarketpy, added dataframe input for TradeAnalysis, fixed typo in readme
• 07 Oct 2016 - Add .idea to .gitignore
• 06 Oct 2016 - Split out plotting of no of trades and position proportion
• 22 Sep 2016 - Fixed sorting of columns when signal plotting
• 21 Sep 2016 - Allow plotting of multiple signal days
• 15 Sep 2016 - Merged finmarketpy and pythalesians fully, released version 0.11
• 12 Sep 2016 - Fixed issue with TradeAnalysis (method names)
• 02 Sep 2016 - Fixed issue with external dataframe eco events, added event study example
• 01 Sep 2016 - Added seasonality example for FX vol
• 22 Aug 2016 - Fixed boot issue and added credentials
• 17 Aug 2016 - Uploaded first code

• 03 Aug 2016 - Fixed missing conf files
• 02 Aug 2016 - Changed default Plotly background color and fixed constants issue with AdapterTemplate
• 01 Aug 2016 - Renamed pythalesians_graphics as chartesians (preparing eventual spinout)
• 29 Jul 2016 - Created Jupyter notebook plot_market_data for plotting with multiple libraries, also fixed Bokeh sizing issue, refactored library, spinning out chart functionality into pythalesians_graphics
• 28 Jul 2016 - Fixed issue with multiple fields returned by Quandl, added Quandl downloading example
• 26 Jul 2016 - Added more support for Plotly charts, added surface vol Plotly example
• 21 Jul 2016 - Refactor StrategyTemplate graph plotting functions
• 20 Jul 2016 - Return of figure handle for AdapterPyThalesians
• 08 Jun 2016 - Fix kurtosis issue, refactored vol scaling in CashBasktest, added resample wrapper in TimeSeriesFilter
• 03 Jun 2016 - Speed up CashBacktest (construct_strategy method)
• 02 Jun 2016 - Fixed missing StrategyTemplate file in installation, added auto-detection of path to simplify installation and added methods for converting between pandas and bcolz
• 31 May 2016 - Got rid of deprecated Pandas methods in TechIndicator
• 27 May 2016 - Added ability to plot strategy signal at point in time
• 19 May 2016 - Updated Quandl wrapper to use new Quandl API
• 02 May 2016 - Tidied up BacktestRequest, added SPX seasonality example
• 28 Apr 2016 - Updated cashbacktest (for Pandas 0.18)
• 21 Apr 2016 - Got rid of deprecated Pandas methods in EventStudy
• 18 Apr 2016 - Fixed some incompatibility issues with Pandas 0.18
• 06 Apr 2016 - Added more trade statistics output
• 01 Apr 2016 - Speeded up joining operations, noticeable when fetching high freq time series
• 21 Mar 2016 - Added IPython notebook to demonstrate how to backtest simple FX trend following trading strategy
• 19 Mar 2016 - Tested with Python 3.5 64 bit (Anaconda 2.5 on Windows 10)
• 17 Mar 2016 - Refactored some of graph/time series functions and StrategyTemplate
• 11 Mar 2016 - Fixed warnings in matplotlib 1.5
• 09 Mar 2016 - Added more TradeAnalysis features (for sensitivity analysis of trading strategies)
• 01 Mar 2016 - Added IPython notebook to demonstrate how to download market data and plot
• 27 Feb 2016 - Fixed total returns FX example
• 20 Feb 2016 - Added more parameters for StrategyTemplate
• 13 Feb 2016 - Edited time series filter methods
• 11 Feb 2016 - Added example to plot BoJ interventions against USDJPY spot
• 10 Feb 2016 - Updated project description
• 01 Feb 2016 - Added LightEventsFactory to make it easier to deal with econ data events (stored as HDF5 files)
• 20 Jan 2016 - Added kurtosis measure for trading strategy results, fixed Quandl issue
• 19 Jan 2016 - Changed examples folder name
• 15 Jan 2016 - Added risk on/off FX correlation example
• 05 Jan 2016 - Added total return (spot) indices construction for FX and example
• 26 Dec 2015 - Fixed problem with econ data downloaders
• 24 Dec 2015 - Added datafactory templates for creating custom indicators
• 19 Dec 2015 - Refactored Dukascopy downloader
• 10 Dec 2015 - Various bug fixes
• 22 Nov 2015 - Increased vol targeting features for doing backtesting
• 07 Nov 2015 - Added feature to download tick data from Bloomberg (with example)
• 05 Nov 2015 - Added intraday event study class (and example)
• 02 Nov 2015 - Added easy wrapper for doing rolling correlations (and example)
• 28 Oct 2015 - Added more sensitivity analysis for trading strategies
• 26 Oct 2015 - Various bug fixes for Bloomberg Open API downloader
• 14 Oct 2015 - Added capability to do parallel downloading of market data (thread/multiprocessing library), with an example for benchmarking and bug fixes for Bloomberg downloader
• 25 Sep 2015 - Refactored examples into different folders / more seasonality examples
• 19 Sep 2015 - Added support for Plotly choropleth map plots & easy downloading of economic data via FRED/Bloomberg/Quandl
• 12 Sep 2015 - Added basic support for PyFolio for statistical analysis of strategies
• 04 Sep 2015 - Added StrategyTemplate for backtesting (with example) & bug fixes
• 21 Aug 2015 - Added stacked charts (with matplotlib & bokeh) & several bug fixes
• 15 Aug 2015 - Added bar charts (with matplotlib & bokeh) & added more time series filter functions
• 09 Aug 2015 - Improved Bokeh support
• 07 Aug 2015 - Added Plotly support (via Jorge Santos Cufflinks wrapper)
• 04 Aug 2015 - Added ability to download from FRED and example for downloading from FRED.
• 29 Jul 2015 - Added backtesting functions (including simple FX trend following strategy) and various bug fixes/comments.
• 24 Jul 2015 - Added functions for doing simple seasonality studies and added examples.
• 17 Jul 2015 - Created example to show how to use technical indicators.
• 13 Jul 2015 - Changed location of conf, renamed examples folder to pythalesians_examples. Can now be installed using setup.py.
• 10 Jul 2015 - Added ability to download Dukascopy FX tick data (data is free for personal use - check Dukascopy terms & conditions). Note that past month of data is generally not made available by Dukascopy

End of note