Quickstart

This tutorial serves as an overview of the main functionality of the Garpar system.

Imports

There are two important subpackages in Garpar:

• optimize allows applying optimization models.

• datasets contains tools for market simulation.

You would generally import them as:

from garpar import datasets, optimize

The StocksSet class

Most of the time the system will interact between modules with instances of the StocksSet class. This has the following representation:

datasets.make_risso_normal()

Stocks	S0[W 1.0, H 0.5]	S1[W 1.0, H 0.5]	S2[W 1.0, H 0.5]	S3[W 1.0, H 0.5]	S4[W 1.0, H 0.5]	S5[W 1.0, H 0.5]	S6[W 1.0, H 0.5]	S7[W 1.0, H 0.5]	S8[W 1.0, H 0.5]	S9[W 1.0, H 0.5]
Days
0	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000
1	99.255892	98.643424	101.182556	100.976145	101.142664	99.229344	98.820761	99.197104	100.881702	100.798837
2	98.403726	97.941513	102.689855	100.055446	100.220380	98.205988	97.837035	99.885786	100.130212	99.650853
3	97.739785	99.010624	101.689523	100.938276	99.031921	99.265086	96.822695	101.121811	99.251223	98.828421
4	97.012671	98.355857	102.628715	100.388073	97.985321	98.237598	97.643243	99.908517	98.337190	99.621729
...	...	...	...	...	...	...	...	...	...	...
361	84.350353	103.526064	97.581859	121.705135	84.452154	99.770152	121.838193	80.917318	138.589922	96.987257
362	83.722518	102.315907	98.734285	122.937505	83.512030	98.965622	122.643050	81.689852	140.146500	95.750841
363	82.865639	101.536066	97.587011	124.154504	82.554307	99.997622	123.731972	82.316293	139.043815	96.653080
364	83.772011	103.128469	96.521585	122.928053	83.382551	99.131031	121.950915	81.557621	140.198926	95.810688
365	82.980717	101.904244	95.531620	121.637567	84.322867	98.034875	120.471671	82.182295	138.488500	94.909318

366 days x 10 stocks - W.Size 5

A StocksSet instance unifies the representation of a market and a portfolio, simplifying stock tracking and weight (W) management. It streamlines data handling and serves as a shared concept across all system modules. A market reflects the current stock values, while a portfolio consists (in this context) of assigned weights for each stock. We will explore these components in more detail throughout the tutorials. For now, let’s start with a basic example.

In this tutorial, we will simulate a market and apply an optimization model to demonstrate basic interactions with the system, providing an introduction to its functionality.

We can simulate a market by calling the make_risso_normal function from the datasets subpackage. This function will create an instance of StocksSet through a simulation. For consistency, we’ll specify just one parameter: random_state, which acts as the seed.

ss = datasets.make_risso_normal(random_state=23)
ss

Stocks	S0[W 1.0, H 0.5]	S1[W 1.0, H 0.5]	S2[W 1.0, H 0.5]	S3[W 1.0, H 0.5]	S4[W 1.0, H 0.5]	S5[W 1.0, H 0.5]	S6[W 1.0, H 0.5]	S7[W 1.0, H 0.5]	S8[W 1.0, H 0.5]	S9[W 1.0, H 0.5]
Days
0	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000
1	100.766064	101.126354	101.056170	100.520483	99.278068	101.231624	101.454794	101.363444	100.618480	98.850608
2	101.767974	100.260879	100.115244	99.516454	98.228567	100.214519	102.472212	100.074498	101.245075	98.005358
3	103.171863	101.187614	99.261945	100.561238	97.054380	99.457204	101.534985	100.942186	100.124496	97.030506
4	104.240403	102.423134	98.378702	101.551601	95.927476	100.246909	102.232905	100.196595	98.964000	96.428484
...	...	...	...	...	...	...	...	...	...	...
361	104.845268	121.219172	131.868621	104.611151	142.212483	106.411823	100.923496	92.259337	120.218776	90.228541
362	105.897108	122.295360	130.769070	103.710214	143.863287	107.679350	102.327765	93.440659	121.197158	91.080514
363	107.368403	123.454928	132.064844	104.970441	142.355648	108.778114	103.399001	92.368845	120.270729	90.469203
364	108.435637	122.318475	133.215786	105.868644	140.662842	107.785096	104.275516	91.656621	121.732478	91.496238
365	107.026179	121.488850	134.581393	104.821875	139.648724	109.153324	105.619600	90.699211	123.443885	92.091881

366 days x 10 stocks - W.Size 5

Now that we have a StocksSet instance, let’s apply an optimization model and observe how the weights change. To analyze the effect, focus on the Ws in the top section of the StocksSet representation and compare them with their updated values after applying the following model:

mk = optimize.mean_variance.Markowitz(target_risk=0.1)
mk.optimize(ss)

Stocks	S0[W 3.711507e-09, H 0.5]	S1[W 7.229484e-03, H 0.5]	S2[W 3.709488e-01, H 0.5]	S3[W 2.995300e-09, H 0.5]	S4[W 5.030012e-01, H 0.5]	S5[W 4.103622e-09, H 0.5]	S6[W 3.458309e-09, H 0.5]	S7[W 1.305153e-09, H 0.5]	S8[W 1.188205e-01, H 0.5]	S9[W 1.288363e-09, H 0.5]
Days
0	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000	100.000000
1	100.766064	101.126354	101.056170	100.520483	99.278068	101.231624	101.454794	101.363444	100.618480	98.850608
2	101.767974	100.260879	100.115244	99.516454	98.228567	100.214519	102.472212	100.074498	101.245075	98.005358
3	103.171863	101.187614	99.261945	100.561238	97.054380	99.457204	101.534985	100.942186	100.124496	97.030506
4	104.240403	102.423134	98.378702	101.551601	95.927476	100.246909	102.232905	100.196595	98.964000	96.428484
...	...	...	...	...	...	...	...	...	...	...
361	104.845268	121.219172	131.868621	104.611151	142.212483	106.411823	100.923496	92.259337	120.218776	90.228541
362	105.897108	122.295360	130.769070	103.710214	143.863287	107.679350	102.327765	93.440659	121.197158	91.080514
363	107.368403	123.454928	132.064844	104.970441	142.355648	108.778114	103.399001	92.368845	120.270729	90.469203
364	108.435637	122.318475	133.215786	105.868644	140.662842	107.785096	104.275516	91.656621	121.732478	91.496238
365	107.026179	121.488850	134.581393	104.821875	139.648724	109.153324	105.619600	90.699211	123.443885	92.091881

366 days x 10 stocks - W.Size 5

The change in weights is what we aim for when applying optimization models.

This concludes the quickstart. We recommend continuing with this tutorial.