Using Dask for large systems of financial models

Talk at PyData NYC 2017

Transcript

1. Using Dask for large systems of financial models Petr Wolf, Dhivya Shankaranarayan PyData NYC 2018
2. 2 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted The views expressed here are those of the authors and do not necessarily represent or reflect the views of Barclays Disclaimer
3. 3 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted 1. Financial models for Planning and Stress-Testing 2. Modular approach to model development and integration 3. Benefits of an integrated solution 4. Notebook example 5. Role of Open Source Software Agenda
4. 4 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted Contacts • https://www.linkedin.com/in/dhivyanarayan • https://www.linkedin.com/in/petrwolf Slides • https://speakerdeck.com/petrwolf/using-dask-for-large-systems-of-financial- models Notebook • https://github.com/PetrWolf/pydata_nyc_2018 Links
5. 5 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted Financial planning - to model the change in firm’s financial situation over time, based on various inputs and scenarios Stress testing – to “measure the resilience of banks to hypothetical adverse scenarios”1 Specific requirements • Robust development and internal validation processes • Transparent methodologies for business sign-off of models and projected results Financial models for Planning and Stress-Testing 1 Dent, Kieran & Westwood, Ben & Segoviano, Miguel - Stress testing of banks: an introduction, Bank of England Quarterly Bulletin, vol. 56(3)
6. 6 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted • Multiple platforms with silo-ed model development and execution • Manual & error-prone execution • Lack of reproducibility and complete audit trail • Limited ability to perform end-to-end sensitivity and what-if analysis Traditional organizations do not scale up to modern requirements Business Planning Stress Testing Risk Macro- Economic Factors Current Financials Manual Overrides Business 1 models Business 2 models Business 3 models Ad-hoc Model Development
7. 7 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted Typical modeling approach mixes several separate concerns • Data processing, validation modeling logic, orchestration, reporting and visualization co- mingled. • Potential inconsistency across model inputs and outputs • Lack of clarity on units, benchmarks and assumptions
8. Modular approach for a productive model development and flexible integration process
9. 9 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted 1. Establish shared conventions – Model using pure functions (aligns with Dask best practices) – Define “public” data labels as interface to/from functions 2. Extend syntax for easier development – Annotate functions (using decorators) to label inputs and outputs 3. Automate composition – Automatically connect functions into Dask graphs based on data dependencies (using Custom Graph API) Modular approach to model development and integration
10. 10 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted Automated dependency graphs with discoverable interface Market Share f Industry Size Volume Volume Margin Profit g Volume … …
11. 11 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted Keep a central definition of all data elements: • Industry Size (int, units: “mUSD”): Annual volume on the US market • Market Share (float, units: “%”): … • Volume … • Margin… • Profit … Global Data Dictionary for consistency and clarity Market Share Volume f Industry Size Margin Profit g
12. 12 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted • Dask is a light-weight open-source library for parallel computing in Python • Scales from a laptop to large clusters (1000s of cores) • Popular in Python scientific community1, including banks2 • High-level “pandas-like” interfaces, integration in scikit_learn • Exposes Custom Graph API Dask exposes its low-level interface for direct graph creation Custom Graph API 1 https://dask-stories.readthedocs.io 2 https://www.anaconda.com/blog/developer-blog/credit-modeling-with-dask/
13. 13 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted The tale of 3 APIs High-level API using dask.dataframe df = dask.dataframe.read_csv('*.csv') df.groupby(df.account_id)\ .balance\ .sum() df.compute()
14. 14 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted The tale of 3 APIs Explicit graph creating unig Custom Graph API: def inc(x): return x + 1 def mul(x, y): return x * y graph = { 'i': (inc, 5), 'j': (mul, 'i', 7), 'k': (mul, 'i', 'j'), } dask.get(graph, 'j')
15. 15 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted @model(ind_size="Industry Size", mkt_share="Market Share", output="Volume"): def f(ind_size, mkt_share) : return ind_size * mkt_share Extend python functions with annotations of inputs/outputs internal parameter name external data label ↓ generate Dask graph entry {"Volume": (f, "Industry Size", "Market Share")} Market Share f Industry Size Volume
16. 16 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted The tale of 3 APIs Automatic graph generation using @model @model(ind_size="Industry Size", ..., output="Volume"): def f(ind_size, mkt_share): return ind_size * mkt_share @model(vol="Volume", margin="Margin", output="Profit"): def g(vol, margin): return vol * margin graph = join(f, g, ...) dask.get(graph, 'Profit')
17. 17 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted High Level API Custom Graph API Generated Graph Functions def inc(x): return ... def mul(x, y): return ... @model(ind_size=...): def f(ind_size, mkt_share): return ... @model(vol=...): def g(vol, margin): return ... Structure df = dd.read_csv('*.csv') df.groupby(df.account_id)\ .balance\ .sum() graph = { 'i': (inc, 5), 'j': (mul, 'i', 7), 'k': (mul, 'i', 'j') ... } graph = join(f, g, h, ...) The tale of 3 APIs
18. 18 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted Bottom-up composition for incremental build-out Function Model Ensemble Single unit of modeling logic (formula, calibrated model, business process) Multiple functions connected via data dependencies, representing products or business segments Models integrated together to represent entire business units, legal entities or modeling areas. Unit tests Regression tests Integration tests Z = X+Y X Y Z
19. Benefits Advantages of an integrated solution
20. 20 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted  Automated connections make changes simple: – Compose, break up, re-use, replace – Champion/Challenger, version upgrade/rollback  Separation of code/data make testing flexible: – Use of different inputs and environments (development, production)  Open to adjustments and extensions – “Overrides” (abs. or rel.) to selected nodes – Node/edge properties (units, types, …) Combined dependency graph for flexible development
21. 21 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted • Dependency analysis, • Sensitivity testing (external inputs and internal assumptions or intermediate values), • Back-testing, • “What-if” analysis, • Reverse Stress-testing, • Support in decision making, • Reinforcement-learning Integrated framework for end-to-end analysis and robust execution Macro Economic Variables Overrides Financials Business 1 models Business 2 models Business 3 models Scenario analysis Sensitivity analysis … Business Planning Stress Testing … Risk Integrated Framework Review & Analysis
22. Example Sample code using Dask
23. 23 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted Huge thanks to OSS maintainers of modeling libraries (Pandas, NumPy, SciPy, Dask, NetworkX), tools (jupyter , Sphinx, PyLint, PyTest) and many others Role of OSS in large financial corporations has changed and is accelerating How you can help? Consumption Highlight use of open source in projects Contribution Establish a firm-wide policy Publication Find suitable pilot projects Prohibited Isolated Supported Encouraged
24. 24 | Using Dask for large systems of Financial models | PyData NYC 2018 Unrestricted • Large financial modeling projects face unique challenges arising from a disparity of models and data • Strict rules and technology can help in building a flexible development and execution infrastructure • Automation opens doors to further analysis and re-use that would otherwise not be possible • Open source is a key enabler for even small teams to build great projects quickly Summary
25. Thank you! Q&A