computational-graph on SimianQuant
https://www.simianquant.com/categories/computational-graph/
Recent content in computational-graph on SimianQuantHugo -- gohugo.ioenWed, 23 Oct 2019 00:00:00 +0000Conditional Graph Factorization
https://www.simianquant.com/blog/conditionalgraphfactorization/
Wed, 23 Oct 2019 00:00:00 +0000https://www.simianquant.com/blog/conditionalgraphfactorization/Conditional Graph Factorization is a submodule of the SimianQuant code generator that generalizes the concept of calibration. Given a sequence of partitions of the domain, it factorizes the full computational graph into a sequence of subgraphs conditional on the respective partition and the inferred codomain of the previous subgraph. This article illustrates the principle and some applications.
Concretely, given a computational graph $\Bbb G(\Bbb X)$ with its input partitioned into a sequence of $n$ subsets $x_i, i\in {1 \dots n}$, conditional graph factorization generates a sequence of subgraphs $g_i$ such that:Technical Indicators using SimianQuant
https://www.simianquant.com/blog/technicalindicatorbenchmark/
Tue, 14 May 2019 00:00:00 +0000https://www.simianquant.com/blog/technicalindicatorbenchmark/The SimianQuant library provides a Fluent API on top of its symbolic engine to implement functions with feedback. Technical indicators are a classic example of the usage of this pattern. This article presents the results of benchmarking the generated implementations (in C++) of common indicators.
To summarise:
The runtime performance on commodity hardware is in the single/double digit nanosecond range, and comparable with custom hardware The runtime cost of evaluating multiple indicators together is less than the total cost of evaluating them separately.Graph Compression Applied to Quantitative Finance
https://www.simianquant.com/blog/graphcompressionquantitativefinance/
Tue, 19 Mar 2019 00:00:00 +0000https://www.simianquant.com/blog/graphcompressionquantitativefinance/The sequence of operations that convert input to output can be visualized as a graph. Graph compression is the family name for a suite of SimianQuant algorithms that rewrite the computational graph to reduce the time and/or memory required.
At a conceptual level, they are similar to Common Subexpression Elimination. However, since this is not your grandfather’s compiler, the compression is semantic, i.e. based on the algebraic properties of the graph.