I-Quantitative Algorithm: Izihluthulelo Eziyisi-7 Zokuba Ingcweti Ukuhweba Okuzenzakalelayo

Informatec Digital » Ama-algorithms » I-Quantitative Algorithm: Izihluthulelo Eziyisi-7 Zokuba Ingcweti Ukuhweba Okuzenzakalelayo

I-athikili ehlobene:

Izinhlobo eziyinhloko ze-algorithm zichazwe ngendlela elula

Izisekelo zokuhweba nge-algorithmic

Ukuhweba kwe-Algo, okwaziwa nangokuthi ukuhweba nge-algorithmic, kuyisisekelo lapho ama-algorithms omthamo akhelwe phezu kwawo. Le ndlela yokuhweba ezimakethe zezimali isebenzisa izinhlelo zekhompiyutha ukwenza ukuthenga nokuthengisa ama-oda ngokuya ngesethi echazwe ngaphambilini yemithetho nemiyalo.

Yiziphi izinzuzo zokuhweba nge-algorithmic? Okokuqala, isivinini. Ama-algorithms angacubungula amanani amakhulu edatha futhi enze izinqumo ngama-millisecond, into engeke yenzeke kumuntu. Ukwengeza, basusa isici somzwelo ekuhwebeni, okungaba inzuzo enkulu ngezikhathi zokuguquguquka okukhulu.

Enye inzuzo ebalulekile amandla okubuyisela emuva. Abahwebi bangahlola amasu abo kudatha yomlando ngaphambi kokufaka imali yangempela engozini, okuvumela ukuthuthukiswa kwamasu okuqhubekayo.

Izingxenye ezibalulekile ze-algorithm yobuningi

I-algorithm ye-quantitative esebenzayo iqukethe izici ezimbalwa ezibalulekile:

1. Ukuqoqwa kwedatha: Isinyathelo sokuqala ukuthola idatha efanele, yekhwalithi ephezulu. Lokhu kungase kuhlanganise amanani omlando, amanani okuhweba, izinkomba zomnotho nezindaba zezimali.
2. Ukuhlaziywa kwezibalo: Ama-algorithms asebenzisa amasu ezibalo athuthukisiwe ukukhomba amaphethini nokuhlobana kudatha.
3. Ukumodela okuqagelayo: Ngokusekelwe ekuhlaziyweni, amamodeli athuthukiswa ukubikezela ukunyakaza kwezimakethe zesikhathi esizayo.
4. Imithetho yokuhweba: Imithetho ecacile isungulwe yokungena nokuphuma ezikhundleni ngokusekelwe kumasignali akhiqizwa imodeli.
5. Ukulawulwa kobungozi: Kusetshenziswa izindlela zokulawula nokukhawulela ukulahlekelwa okungaba khona.
6. Qalisa ngokuzenzakalelayo: I-algorithm kufanele ikwazi ukwenza ama-oda ngokuzenzakalelayo ngesikhathi sangempela.

Amasu Adumile Ekuhwebeni Okulinganiselwe

Kunamasu amaningi abahwebi abawasebenzisayo ukuze bathole umkhawulo ezimakethe. Ezinye ezivame kakhulu zihlanganisa:

• I-Arbitrage yezibalo: Leli su lihlose ukuxhaphaza ukungasebenzi kahle kwamanani phakathi kwezimpahla ezihlobene.
• Ukuhweba okunomfutho: Kusekelwe embonweni wokuthi izimpahla ebezikhuphuka (noma eziwayo) zizoqhubeka zenze kanjalo esikhathini esifushane.
• Kusho ukuguqulwa: Leli su lithatha ngokuthi amanani nezinye izinkomba ekugcineni zizobuyela esilinganisweni sazo somlando.
• Ukwenza imakethe: Ama-algorithms ahlinzeka nge-liquidity emakethe, enza inzuzo ngokusabalalisa phakathi kwamanani okuthenga nokuthengisa.

Ngalinye lalawa masu lidinga i-algorithm ethile yobuningi, eklanyelwe ukuthola nokusebenzisa amathuba athile indlela ngayinye efuna ukuwasebenzisa.

Amathuluzi nezilimi zokuhlela zokuthuthukisa ama-algorithms

Ukuze bakhe ama-algorithms asebenzayo, abahwebi badinga ukwazi amathuluzi athile nezilimi zokuhlela. Ezinye ezidume kakhulu yilezi:

1. IPython: Ngenxa yobulula bayo nemitapo yolwazi enamandla njengeNumPy nePandas, iPython isiwulimi olukhethwayo ngamanani amaningi.
2. R: Iwusizo ikakhulukazi ekuhlaziyweni kwezibalo kanye ukubonwa kwedatha.
3. MATLAB: Isetshenziswa kabanzi ezifundweni nasezicwaningweni zezezimali.
4. I-C ++: Uma isivinini sokwenza sidingeka, i-C++ ihlala iyinketho ekhethwayo.

Ngokungeziwe kulezi zilimi, kunezinkundla ezikhethekile ezifana ne-MetaTrader ne-QuantConnect ezisiza ukuthuthukiswa nokubuyiselwa emuva kwe-algorithms yobuningi.

Ukuhlola imuva kanye nokwenza kahle kwe-quantitative algorithms

Ukuhlola emuva kuyinqubo ebalulekile ekuthuthukisweni kwama-algorithms ezibalo. Kuhilela ukuhlola isu kudatha yomlando ukuhlola ukusebenza kwayo okungenzeka. Kodwa-ke, kubalulekile ukucabangela ingozi yokufaka ngokweqile i- , lapho i-algorithm ivumelaniswa kakhulu nedatha yomlando futhi ilahlekelwa ikhono layo lokubikezela ngedatha yesikhathi esizayo.

Ingenziwa kanjani i-algorithm yobuningi? Nazi izinyathelo ezibalulekile:

1. Qala ngesu elilula futhi kancane kancane wengeze inkimbinkimbi.
2. Sebenzisa amasethi edatha yokuqeqeshwa nokuqinisekisa ahlukene.
3. Yenza ukuhlaziywa kokuzwela ukuze uqonde ukuthi izinguquko zepharamitha zikuthinta kanjani ukusebenza.
4. Sebenzisa amasu we ukuhamba phambili nokwenza kahle ukulingisa izimo zokuhweba ngesikhathi sangempela.

Ukulawulwa kobungozi ekuhwebeni kwe-algorithmic

Ukulawulwa kobungozi kuyingxenye ebalulekile yanoma yiliphi isu lokuhweba, futhi ama-algorithms obuningi nawo awahlukile. Amanye amasu ajwayelekile ahlanganisa:

• Ukulahleka kokumiswa okuzenzakalelayo: Ukukhawulela ukulahlekelwa ezikhundleni ngazinye.
• Ukuhlukahluka: Sabalalisa ubungozi kuzo zonke izimpahla namasu ahlukene.
• Ukulawula ukuchayeka: Khawulela usayizi ophelele wezindawo ezivuliwe.
• Ukuqapha okudonswa phansi: Beka imikhawulo ekulahlekelweni okunqwabelene.

Kubalulekile ukuthi lezi zindlela zokulawula ubungozi zihlanganiswe ngokuqondile ku-algorithm yobuningi ukuze kuqinisekiswe ukusetshenziswa kwazo okungaguquki nokuzenzakalelayo.

Izinselele kanye Nemikhawulo Yokuhweba Okuningi

Naphezu kwezinzuzo zakho eziningi, ukuhweba ngobuningi nakho kubhekana nezinselele ezibalulekile:

1. Ukuncika kwedatha yomlando: Ama-algorithms acabanga ukuthi amaphethini adlule azoziphinda, okungelona iqiniso ngaso sonke isikhathi.
2. Ingozi yemodeli: Imodeli echazwe kabi ingaholela ezinqumweni ezingezinhle zokuhweba.
3. Ukuncintisana okukhulu: Njengoba abathengisi abaningi besebenzisa izindlela zokulinganisa, amathuba e-arbitrage ayancipha.
4. Izindleko zobuchwepheshe: Ukuthuthukisa nokugcina ingqalasizinda yokuhweba ye-algorithmic kungabiza.
5. Izingozi zokusebenza: Amaphutha kukhodi noma ukwehluleka ezinhlelweni kungaholela ekulahlekelweni okukhulu.

Amathrendi wesikhathi esizayo kuma-algorithm obuningi

Inkambu ye-quantitative algorithms ishintsha njalo. Amanye amathrendi asafufusa ahlanganisa:

• I-Artificial Intelligence kanye nokufunda okujulile: Lobu buchwepheshe buvumela ukuthuthukiswa kwe-algorithms eyinkimbinkimbi futhi eguquguqukayo.
• Idatha enkulu: Ikhono lokucubungula nokuhlaziya amasethi edatha ngokuya ngokuba makhudlwana livula amathuba amasha.
• Ukuhweba okuvama kakhulu: Intuthuko kwezobuchwepheshe iholela kumasu asebenza esikalini sesikhathi esifushane.
• Ama-algorithms amaningi kuma-cryptocurrencies: Ukukhula kwemakethe ye-cryptoasset kudala amathuba amasha amasu e-algorithmic.

Izindaba zempumelelo nezifundo ezifundiwe

Eminyakeni edlule, kube nezindaba eziningi zempumelelo ekusetshenzisweni kwe-algorithms yobuningi. Enye eyaziwa kakhulu i-Renaissance Technologies, i-hedge fund eye yazuza imbuyiselo emangalisayo amashumi eminyaka isebenzisa amasu obuningi.

Nokho, kube khona nokwehluleka okuphawulekayo. Ukuwa Kokuphathwa Kwezimali Zesikhathi Eside ngo-1998 kuyisikhumbuzo sobungozi obuhlobene nokusetshenziswa ngokweqile nokuthembela kumamodeli ezibalo ayinkimbinkimbi.

Izifundo ezibalulekile ezivela kulezi zimo zihlanganisa:

1. Ukubaluleka kokulawulwa kobungozi okuqinile.
2. Isidingo sokuqonda ukulinganiselwa kwamamodeli.
3. Ukubaluleka kokuvumelana nezimo endaweni yemakethe eshintshayo.

Isiphetho: Ingabe I-Quantitative Algorithm iyikusasa Lokuhweba?

I-algorithm yobuningi ngokungangabazeki iguqule isimo sokuhweba ngezezimali. Ikhono layo lokucubungula inani elikhulu ledatha, ukusebenzisa amasu ayinkimbinkimbi kanye nokuhweba ngaphandle kokukhathala kukwenze kwaba ithuluzi elibalulekile kubahlanganyeli abaningi bemakethe.

Kodwa-ke, akulona ikhambi. Ama-algorithms ezibalo amahle kuphela njengedatha namamodeli asekelwe kuwo, futhi kuyohlala kunendima yokwahlulela komuntu ekuchazeni imiphumela nokwenza izinqumo zamasu. Ukuze ujule ngokujulile eziyisisekelo kanye nemibono ethuthukisiwe yokufunda komshini, ungavakashela lokhu okuqukethwe ekufundeni komshini.

Ikusasa cishe lizobona ukuhlanganiswa okukhulu nakakhulu kwezindlela zokulinganisa nekhwalithi, nabahwebi babantu abasebenza eduze nezinhlelo ze-algorithmic ezithuthukayo. Kulabo abazimisele ukutshala isikhathi nezinsiza ezidingekayo ukuze bakwazi lobu buchwepheshe, ama-algorithms omthamo anikeza inzuzo enamandla ezimakethe zezimali zesimanje.

Ingabe uthole lesi sihloko se-algorithms yobuningi siwusizo? Uma kunjalo, zizwe ukhululekile ukwabelana ngakho nozakwenu nabangane abathanda ukuhweba nezezimali. Ngokubambisana singaqhuba ukwamukelwa kwamasu okuhweba athuthuke kakhulu futhi asebenza ngempumelelo!