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9

For example, Thomas H. Cormen, Charles E. Leiserson, Ronald Rivest, Clifford Stein. Introduction to Algorithms, problem 24-3 says: 24-3 Arbitrage Arbitrage is the use of discrepancies in currency exchange rates to transform one unit of a currency into more than one unit of the same currency. For example, suppose that 1 U.S. dollar buys 49 Indian rupees, 1 ...


3

I feel this is not a duplicate of a question asking about applications of graph theory as this goes the other way. If you're talking purely about currency arbitrage, the quickest way seems to be finding a negative cycle in a graph of currency where the vertices are the currencies and the nodes the exchange rate.


11

Why do people suggest using red black trees/balanced binary trees for the levels in a limit order book? Because people are unoriginal and keep referencing the same blog post. Why are they algorithmically ideal? They're not necessarily ideal. In fact, they're rarely used in production trading systems with low latency requirements. However, your source ...


2

There is a difference about understanding LOB dynamics and using an algorithmic solution to capture these dynamics. How LOB evolves. We understood now long ago (see Jeremy Large's papers) that a Markov chain on "pictures" of the LOB would be an interesting model. After few years of modeling LOB dynamics with Hawkes processes (see for instance ...


1

NilssonHedge.com offers access to daily return data. Full disclosure, I am the owner of the site.


1

IMHO, the best data provider outside of Bloomberg for those who are comfortable with programmatic access is TickData.com. You can get enormous amounts of future and stock data. They have API access. You can customise how you do rolls. They have an API. You can work with AWS. It's professional grade data with professional access and data cleaning / ...


1

Market data for professional users is generally much more expensive than retail so Bloomberg might not be a bad deal in the end. As for alternatives consisting of both data and data store: $0 for Axibase TSD on single node (my affiliation) $699 + exch. fees for polygon.io delayed feed for all US exchanhes (no affiliation) Another idea, at least for ...


0

If you are a quant fund, algoseek.com is the data vendor worth checking out. Algoseek provides comprehensive, professional intraday and end-of-day market data products designed especially for quants and machine learning. For example, their equity Trade+Quote minute bar has around 80 data points, providing detailed information on market dynamics and ...


7

Two chief reasons for subsampling or using a different event space are (i) computational or spatial tractability and (ii) denoising/signal extraction. Sergei's response seems to focus on the first issue, and I'll focus more on the latter. The two objectives can diverge. For example, options and OTC data can exhibit trade to order ratios in excess of 1:10,000,...


2

This depends on the use case, but there are many options including: Convert full order log to top-of-book quotes and trades. This will probably eliminate 90% of ticks in the file. Convert full order log to trades. Even more compression. Take snapshots when you're present in the market, i.e. to measure and optimize your own trade execution quality Take ...


0

I couldn't get it in quadl but found it in https://financialmodelingprep.com/developer/docs#Financial-Statements-Growth


2

Algoseek's data set includes delisted symbols due to bankruptcy, M&A, or any other reasons. They have a sophisticated master file to keep track of historical changes for securities.


1

If the primary motivation is being able to query market data with SQL, take a look at Axibase Time Series Database (my affiliation). Step 1: Sign-up for free Polygon API key. Step 2: Install the database on a Linux machine. Generate API token for POST method to /api/v1/trade-session-summary/import endpoint. Step 3: Download end of day bars for several days ...


4

As always, any answer to this question is hugely driven by your use cases. The following is my interpretation and ansatz. Here on QSE and over at the DB exchange, I have seen various approaches to keeping financial / market data - and the corresponding discussions are quite heated... Some suggest simply dumping/reading to/from CSV, others suggest time series ...


1

In fact SQL is not a good idea, only NYSE can delivery 100 GB/day, I suggest you to start looking at MongoDB, take a look at this presentation James Blackburn - Python and MongoDB as a Platform for Financial Market Data


1

you can try this. www.quandl.com


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