1. Mhedziso yeKutengeserana Kwehuwandu
Zvakawanda wokutengesera yabuda seimwe yenzira dzine simba uye itsva dzekutengeserana mumari yanhasi misika. Nekushandisa masvomhu modhi, algorithms, uye huwandu hwakawanda hwe data, traders inogona kuita sarudzo nekukurumidza, zvakanyanya, uye nehupamhi hwechokwadi. Muchikamu chino, tichaisa hwaro hwekunzwisisa kutengeserana kwehuwandu nekutsanangura zvazvinoreva uye nekukurukura kuti sei kunzwisisa kwakasimba kwendima iyi kwakakosha pakubudirira mumisika yemazuva ano.
1.1 Tsanangura Quantitative Trading
Pakati payo, kutengeserana kwehuwandu kunosanganisira kushandisa masvomhu computations uye manhamba emhando yekuona uye kuita mikana yekutengesa mumisika yemari. Panzvimbo yekuvimba neruzivo rwemunhu, manzwiro, kana kutonga kwekutonga, kuwanda traders vanovakira sarudzo dzavo pamaitiro anofambiswa nedata. Aya marongero anowanzo itwa otomatiki kuburikidza nekushandiswa kweakaomesesa algorithms, ayo anobvumira kukurumidza kuuraya uye kugona kugadzirisa huwandu hwakawanda hweruzivo munguva chaiyo.
Izvo zvikamu zvikuru zvekutengesa kwehuwandu zvinosanganisira kushandiswa kwealgorithms, yakakura backtesting ye zvokutengeserana nzira, advanced ngozi manejimendi maitiro, uye kuongororwa kwedata. Chikamu chega chega chinoita basa rakakosha mukuita kwese, kushanda pamwechete kuona mapatani, Maitiro, uye kusashanda zvakanaka pamusika. Chinangwa ndechekushandisa izvi zvisingabatsiri, zvichiita traders kuita purofiti trades ine ngozi shoma.
1.2 Tsanangura kukosha kwekunzwisisa Quantitative Trading
Kukosha kwehuwandu hwekutengesa kuri mukukwanisa kwayo kubvisa zvakarongeka manzwiro kubva mukutengesa maitiro uchibvumira. traders kuita zvimwe zvinangwa uye sarudzo dzine ruzivo. Manzwiro evanhu, akadai sekutya uye makaro, anowanzo tungamira mukusaita sarudzo, kunyanya panguva yekusagadzikana kwemisika. Kutengeserana kwehuwandu kunobatsira kudzikisira nyaya iyi nekuvimba nemitemo yakatemerwa uye maalgorithms ekuraira kutenga nekutengesa sarudzo.
Pamusoro pezvo, kutengesa kwehuwandu kunobvumira scalability, semavhoriyamu makuru e trades inogona kuurayiwa panguva imwe chete pasina kudiwa kwekupindira kwevanhu nguva dzose. Izvi zvakanyanya kukosha mumisika yemvura zhinji, uko kukurumidza kuuraya kwakakosha kutora mikana inobatsira.
Uyezve, kunzwisisa kuwanda kwekutengesa kunoshongedza traders nemidziyo inodiwa kubata misika yakaoma nharaunda. Nekubatanidza nhamba dzemamodhi uye maitiro ekuongorora data, huwandu traders inogona kufanotaura zvirinani mafambiro emusika, maneja ngozi, uye optimize maitiro avo ekutengesa kune akasiyana musika mamiriro. Ruzivo urwu rwakakosha kune traders vanoda kugara vachikwikwidza munzvimbo inokurumidza kushanduka apo nzira dzealgorithmic uye dzinofambiswa nedata dzinotonga.
| Kuonekwa | Tsanangudzo |
|---|---|
| Quantitative Trading | Inoshandisa masvomhu modhi uye algorithms kuita dhata-inotyairwa nzira dzekutengesa. |
| Makiyi Makuru | Algorithms, backtesting, kutarisira ngozi, uye ongororo yedata inoshanda pamwe chete kuona mikana yekutengesa. |
| kukosha | Inobatsira kubvisa kusarerekera kwemanzwiro, inogonesa scalability, uye inopa maturusi ekubata mamiriro akaomarara emusika. |
| Kukosha kune Vatengesi | Equips traders nemaitiro akarongeka ekuita sarudzo zvirinani, manejimendi enjodzi, uye kuita purofiti. |
2. Core Concepts
Kunzwisisa iwo musimboti pfungwa kuseri kwehuwandu hwekutengesa kwakakosha pakugadzira nekuita akabudirira mazano. Mafungiro aya anoumba hwaro pairi traders inogona kuvaka yakaomesesa, inofambiswa nedata nzira. Muchikamu chino, isu tichaongorora zvakakosha zvikamu zvehuwandu hwekutengesa, kusanganisira algorithmic kutengesa, backtesting, risk management, uye data analysis.
2.1 Algorithmic Kutengeserana
Algorithmic kutengeserana ndiyo maitiro ekushandisa otomatiki masisitimu, anozivikanwa sealgorithms, kuita trades zvichibva pamitemo yakafanotsanangurwa. Iyi nzira inobvisa kudiwa kwekupindira kwemaoko, zvichibvumira kukurumidza kuita sarudzo uye kunyatsoshanda trade kuuraya. Kutengeserana kwealgorithmic kunonyanya kukosha munzvimbo dzinokosha nguva uye kurongeka, sezvo maalgorithms anogona ipapo kugadzirisa huwandu hwakawanda hwe data uye kuita. trades maererano.
2.1.1 Tsanangudzo uye Chinangwa
Chinangwa chealgorithmic kutengeserana ndechekuita otomatiki maitiro ekuita sarudzo, kuderedza kudiwa kwekuisa kwevanhu uye kuwedzera kukurumidza uye iko kurongeka. trades. Nekushandisa masvomhu modhi uye mirairo ine coded, algorithms inogona kutarisa mamiriro emusika uye kupindura kune shanduko mune chaiyo-nguva. Aya maalgorithms akagadzirirwa kuona mikana inobatsira nekuongorora data remusika, sekufamba kwemitengo, vhoriyamu, uye mafambiro. Chinangwa ndechekuita trades panguva yakakodzera uye mutengo, kazhinji nekukurumidza kupfuura munhu trader aigona.
2.1.2 Mhando dzeAlgorithms
Kune akati wandei marudzi ealgorithms anoshandiswa muhuwandu hwekutengesa, imwe neimwe ichishanda yakanangana nechinangwa zvichienderana nehurongwa hunoshandiswa. Rule-based algorithms ari pakati peakajairika, achitsamira pane seti yezvakafanotsanangurwa mamiriro kuita sarudzo dzekutengesa. Izvi zvinogona kusanganisira maitiro akadai sematengo emitengo, moving average crossovers, kana masaini chaiwo emusika.
Statistical algorithms, kune rumwe rutivi, shandisa advanced statistical modhi kuongorora nhoroondo yedata uye kufanotaura mafambiro emitengo yeramangwana. Aya mamodheru anowanzo batanidza dzidziso yekugona kuitika, ongororo yekudzoreredza, uye humwe hunyanzvi hwehuwandu hwekuona mikana yekutengeserana.
Machine kudzidza algorithms inotora iyi nhanho mberi nekubvumira sisitimu "kudzidza" kubva kune nyowani data. Sezvo mamiriro emusika achichinja, muchina kudzidza algorithms anogona kuchinjika nekunatsa mamodheru avo uye nekuvandudza chokwadi chekufungidzira kwavo. Izvi zvinonyanya kubatsira mumisika ine simba apo mapatani uye maitiro anogona kukurumidza kukurumidza.
2.1.3 Mabhenefiti uye Njodzi dzeAlgorithmic Trading
Kutengeserana kwealgorithmic kunopa akati wandei mabhenefiti, kusanganisira kuwedzera kushanda zvakanaka, huchokwadi, uye kugona kugadzirisa huwandu hwakakura hwe data. Nekuita otomatiki maitiro ekutengesa, algorithms anogona kuita trades pakumhanya zvakanyanya kudarika kugona kwevanhu, izvo zvinonyanya kukosha mumisika yemvura zhinji. Pamusoro pezvo, maalgorithms anogona kushanda 24/7, kuve nechokwadi chekuti mikana yekutengesa haipotsa nekuda kwekugumira kwevanhu sekuneta.
Nekudaro, algorithmic kutengeserana inouyawo nenjodzi. Zvisizvo zvakagadzirwa algorithms zvinogona kutungamira mukurasikirwa kukuru kana vakatadza kuzvidavirira kune isingatarisirwe mamiriro emusika kana anomalies. Kune zvakare njodzi yekuwedzeredza, uko algorithm yakanyatso kurongedzerwa kune nhoroondo data uye inoita zvisina kunaka kana ikaiswa kune nyowani data. Chekupedzisira, iyo yekumhanyisa uye otomatiki yealgorithmic yekutengesa dzimwe nguva inogona kuwedzera Misika kusagadzikana, sezvinoonekwa mukuparara kweflash apo automated systems inokonzera kutengesa nokukurumidza.
2.2 Kudzosera kumashure
Backtesting inzira yakakosha mukutengesa kwehuwandu inobvumira traders kuongorora kuti a urongwa hwokutengesa angadai akaita kare. Nekushandisa zano kune nhoroondo yemusika data, traders inogona kuyera kushanda kwayo uye kugadzirisa isati yazviita mumisika mhenyu.
2.2.1 Tsanangudzo uye Maitiro
Backtesting inosanganisira kumhanya hurongwa hwekutengesa kuburikidza nenhoroondo data kuti uone kuti yaizove yakaita sei mumamiriro ezvinhu epasirese. Maitiro acho anowanzo sanganisira kukodha zano kuita backtesting papuratifomu, iyo inozotevedzera iyo trades yakavakirwa pane yapfuura musika data. Izvi zvinopa mukana wakakosha wekuongorora mashandiro ehurongwa, kusanganisira metrics senge purofiti, njodzi, uye kudhirowa.
2.2.2 Kukosha kweBacktesting
Backtesting yakakosha nekuti inobatsira traders vaone kushaya simba muhurongwa hwavo vasati vaisa pangozi mari chaiyo. Nekuongorora maitiro ekare, traders vanogona kukwidziridza mazano avo ekuwedzera purofiti uku vachideredza njodzi. Inobvumirawo traders kunatsiridza maalgorithms avo nekuyedza akasiyana mamiriro, mamiriro emusika, uye paramita, pakupedzisira achiwedzera mukana weiyo nzira yekubudirira mukutengesa kwepamoyo.
2.2.3 Maitiro Akanakisisa ekuBacktesting
Paunenge uchiitisa backtesting, zvakakosha kushandisa data remhando yepamusoro rinonyatsoratidza mamiriro emusika wekare. Kurongeka kwedata kwakakosha, sezvo kusawirirana kupi zvako kunogona kutungamira kumhedzisiro inotsausa. Vatengesi vanofanirawo kushandisa fungidziro dzechokwadi, sekubatanidza mari yekutengeserana uye slippage, kuve nechokwadi kuti backtest Mibairo inoenderana nekuita kwepasirese. Imwe tsika yakakosha ndeyekunzvenga kuwandisa, uko zano rinoita zvakanakisa mukudzokera kumashure asi richikundikana mumisika yehupenyu nekuda kwekunyanya kurongeka kune nhoroondo data.
2.3 Risk Management
Risk management chikamu chakakosha chehuwandu hwekutengesa. Pasina maitiro anoshanda ekugadzirisa njodzi, kunyangwe iyo inonyanya kupa mari yekutengesa algorithms inogona kukonzera kurasikirwa kukuru. Ichi chikamu chinoongorora kukosha kwekutarisira njodzi uye yakakosha metrics yenjodzi inoshandiswa muhuwandu hwekutengesa.
2.3.1 Kukosha kweRisk Management mune Quantitative Trading
Mukutengesa kwehuwandu, kutonga njodzi kwakakosha sekuziva mikana inobatsira. Misika yacho inogara isingatarisike, uye kunyangwe iyo yakanyatso dhizaini algorithms iri pasi pekuchinja kusingatarisirwe kwemusika uye zviitiko zvekunze. Kubudirira kwekutonga kwenjodzi kunobatsira kuchengetedza capital, inova nechokwadi chekugara kwenguva refu, uye kudzivirira kurasikirwa kukuru panguva yekuderera kwemusika.
2.3.2 Risk Metrics
Kugadzirisa njodzi zvinobudirira, huwandu traders kushandisa akati wandei metrics. Value at Risk (VaR) ndeimwe yeanonyanya kushandiswa metrics, kufungidzira kurasikirwa kunogona kuitika mu Portfolio pamusoro penguva yakatarwa pasi pemamiriro ezvinhu emusika. Imwe metric yakakosha Inotarisirwa Kupfupika, iyo inoyera kurasikirwa kweavhareji panguva dzakaipisisa dzekuita kwepodfolio, ichipa kunzwisisa kwakadzama kwenjodzi dzakanyanya.
2.3.3 Njodzi Mitigation Strategies
Vatengesi vanoshandisa nzira dzinoverengeka dzekudzikisa njodzi kuchengetedza mapotifolio avo. Diversification, kana kuparadzira mari mumakirasi akasiyana easset uye misika, inobatsira kuderedza kuratidzwa kune chero njodzi imwe chete. Position saizi ndiyo imwe nzira yakajairika, uko traders kudzikisira saizi yechinzvimbo chega chega zvine chekuita nepotfolio yavo yese kudzikisa kukanganisa kwekurasikirwa kumwe chete. Hedging, kupi traders inotora nzvimbo dzekudzikisa kuderedza njodzi, zvakare inowanzoshandiswa.
2.4 Kuongorora Dhata
Kuongororwa kwedata ibwe rekona rekutengesa kwehuwandu, sezvo ichipa hwaro pakavakirwa nzira dzese dzekutengesa. Nekuongorora nhoroondo uye chaiyo-nguva yemusika data, traders inogona kuona mapatani, maitiro, uye kusashanda kwekushandisa.
2.4.1 Kukosha Kwemhando yeData
Hunhu hwe data hunoshandiswa muhuwandu hwekutengesa hunokosha. Mashoko asina kururama kana asina kukwana anogona kutungamirira kune mhedziso dzisina kunaka uye kusaita zvakanaka kwekutengesa. Kuve nechokwadi chekuti data rakarurama, rakavimbika, uye riri-kusvika-zuva kwakakosha pakuita sarudzo dzine ruzivo uye kugadzira mazano anoshanda.
2.4.2 Kucheneswa kweData uye Preprocessing
Dhata isati yaongororwa, inofanirwa kucheneswa uye kugadziridzwa kuti ibvise zvikanganiso, zvisipo kukosha, uye kunze. Iyi nhanho inovimbisa kuti data inowirirana uye yakakodzera kuongororwa. Preprocessing inogonawo kusanganisira yakajairika data kuti ive nechokwadi chekuti akasiyana dataset anofananidzwa, kunyanya kana ichibatanidza akawanda data masosi.
2.4.3 Data Analysis Techniques
Kune akati wandei data data matekiniki anowanzoshandiswa muhuwandu hwekutengesa. Statistical wongororo inosanganisira kuongorora mafambiro emitengo yenhoroondo kuona mafambiro uye hukama. Nguva yakatevedzana yekuongorora inotarisa pakuongorora mutengo data nekufamba kwenguva, kuona mafambiro, seasonality, uye cyclical mapatani. Aya matekiniki akakosha pakugadzira mamodhi ekufungidzira anozivisa sarudzo dzekutengesa.
| mamiriro | Tsanangudzo |
|---|---|
| Algorithmic Trading | Automated systems executing trades yakavakirwa pamitemo yakafanotsanangurwa; inovandudza kukurumidza uye nekururama. |
| Kudzokorora | Simulation yezano pane nhoroondo data kuongorora kushanda; zvakakosha pakugadzirisa mazano. |
| Risk Management | Matanho ekudzikamisa kurasikirwa kunogona kuitika, kusanganisira kushandiswa kweVaR uye Zvinotarisirwa Shortfall metrics. |
| Data Analysis | Kuongorora data remusika kuona mafambiro uye kusashanda; inovimba nedata rakarurama uye rakafanogadziridzwa. |
3. Mathematics Foundations
Kubudirira kwekutengesa kwehuwandu kunoenderana nemhando dzemasvomhu dzinotsigira nzira dzinoshandiswa. Kunzwisisa kwakasimba kwemisimboti yemasvomhu inosanganisirwa yakakosha pakuvaka nekugadzirisa aya mazano. Ichi chikamu chinoongorora nzira dzechiverengero uye nguva dzakateedzana yekuongorora matekiniki anowanzo shandiswa muhuwandu hwekutengesa.
3.1 Statistical Nzira
Statistical nzira dzinoumba musana wehuwandu hwekutengesa nzira, ichipa maturusi anodiwa kuongorora nhoroondo yedata uye kuita fungidziro nezve remangwana remusika mafambiro. Kushandiswa kwehuwandu hwehuwandu hunobvumira traders kugadzira mamodheru anogona kugadzirisa mavhoriyamu makuru edata, kuona mapatani, uye kuyera mukana wezvakazoitika.
3.1.1 The Probability Theory
Probability theory chikamu chakakosha chekutengesa kwehuwandu, sezvo ichibatsira traders ongorora mukana wezvakasiyana mhedzisiro zvichienderana nenhoroondo data. Nekunzwisisa zvingangoitika, traders inogona kuverengera njodzi uye inogona kudzoka kwavo trades, vachivabatsira kuita zvisarudzo zvine ruzivo. Kunyanya, probability theory inobvumira traders kuverenga zvinotarisirwa kukosha, izvo zviri pakati pekuona kana a trade ine kudzoka kwakanaka kunotarisirwa.
Somuenzaniso, a trader inogona kushandisa mukana wekugovera kufungidzira mukana wemutengo weasset kusvika pane imwe nhanho. Ruzivo urwu runogona kuzobatanidzwa mune algorithm yekutengesa inogadzirisa zvinzvimbo zvichibva pane zvakaverengerwa zvingangoitika.
3.1.2 Hypothesis Kwayedza
Hypothesis test inzira yekuverenga inoshandiswa kuona kana chiitiko chakaonekwa chakakosha kana kuti chingangoitika netsaona. Mukutengesa kwehuwandu, kuongororwa kwekufungidzira kunogona kushandiswa kusimbisa nzira dzekutengesa nekuongorora kana kucherechedzwa kwekuita kwechirongwa kunokonzerwa nekusashanda kwemusika kwechokwadi kana kungochinja-chinja.
Semuenzaniso, a trader inogona kugadzira zano rakavakirwa pafungidziro yekuti mamwe maitiro emitengo anofanotaura mafambiro emutengo mune ramangwana. Kuburikidza nekuongorora kwekufungidzira, iyo trader inokwanisa kuona kuti chiitiko chenhoroondo yechirongwa ichi chaive chakakosha here kana kuti chakabva changoitika. Izvi zvinova nechokwadi chekuti marongero akasimba uye haana kutsamira pane zvisina kujairika, zvenguva pfupi musika maitiro.
3.1.3 Regression Analysis
Regression analysis inzira yenhamba inoshandiswa kuenzanisira hukama pakati pechinhu chinoenderana nechinhu chimwe chete kana kupfuura chakazvimirira. Muchirevo chekutengesa kwehuwandu, regression ongororo inogona kushandiswa kuona uye kuyera hukama pakati pemitengo yeasset nezvimwe zvinosiyana, senge indices yemusika, interest rate, kana zviratidzo zvoupfumi.
Linear regression, imwe yenzira dzakajairika dzekudzokorora kuongororwa, inobvumira traders kuenzanisira hukama pakati pemutengo weasset uye inofanotaura. Nekuongorora regression coefficients, traders inogona kufungidzira kuti shanduko mupredicor variable ichakanganisa sei mutengo weasset, zvichivagonesa kuvaka mamodheru anosanganisira hukama uhu mumaitiro avo ekutengesa.
3.1.4 Statistical Distributions
Kunzwisisa kugoverwa kwenhamba kwakakosha pakuenzanisa uye kufanotaura mafambiro emutengo weasset. Mitengo yeasset inowanzo tevera maitiro ekugovera, akadai seakajairika kana log-yakajairika, ayo anobatsira traders muenzaniso mukana wezvakasiyana mhedzisiro. Mukutengesa kwehuwandu, kugovera kwakajairika kunowanzo shandiswa kuenzanisira kudzoka kweasset, sezvo ichifungidzira kuti shanduko zhinji dzemitengo dzichave diki uye kuti mafambiro akanyanya haawanzo asi zvinogoneka.
Nekunzwisisa chimiro, zvinoreva, uye mwero kutsauka kwekugovera, traders inokwanisa kufungidzira zvirinani mukana wekufamba kwemitengo mune ramangwana uye kugadzirisa njodzi yavo zvinoenderana. Ngozi yemuswe, iyo inoreva njodzi yekufamba kwakanyanyisa kwemusika, inoenzanisirwa zvakare uchishandisa manhamba kugovera, kubatsira. traders gadzirira yakaderera-inogoneka asi yakakwirira-inokanganisa zviitiko.
3.2 Nguva Yakatevedzana Ongororo
Nguva yekutevedzana kuongororwa ndiko kudzidza kwe data data inounganidzwa kana kurekodhwa panguva dzakatarwa dzenguva. Mukutengesa kwehuwandu, nguva yakatevedzana yekuongorora inoshandiswa kuongorora mitengo yeasset uye mamwe dhata rezvemari nekufamba kwenguva kuona mafambiro, mapatani, uye zvinogona kuitika mune ramangwana.
3.2.1 Nguva Yakateedzana Zvikamu
Yenguva yakatevedzana data inoumbwa nezvakati wandei zvakakosha zvikamu: maitiro, mwaka, cyclical mapatani, uye zvisizvo. Kunzwisisa zvikamu izvi kwakakosha pakududzira data remusika uye kufanotaura mafambiro emitengo yeramangwana.
- Trend zvinoreva kufamba kwenguva refu munhevedzano yenguva. Semuyenzaniso, kuenderana kukwira kwemutengo weasset kwemakore akati wandei kunoratidza kukura kwakanaka kwenguva refu.
- Seasonality zvinoreva kudzokorora mapatani kana kuchinjika kunoitika nguva nenguva, semazuva ese, vhiki nevhiki, kana pamwedzi. Mumisika yemari, mwaka unogona kuoneka mukuwedzera kwekutengesa mavhoriyamu pakupera kwekota yemari.
- Cyclical mapatani dzakafanana nemwaka asi dzinoitika pane dzimwe nguva dzisina kujairika, dzinowanzo sungirirana kumatunhu makuru ehupfumi sekuderera kana kuwedzera.
- Zvisingaitwe taura kune zvisingatarisirwi, zvakasiyana-siyana mumutsara wenguva, zvinowanzokonzerwa nezviitiko zvisingafanoonekwi zvemusika kana kunze kwekuvhundutsa.
Nekubvisa data yenguva muzvikamu izvi, traders vanogona kunzwisisa zvirinani masimba ari pasi pekufamba kwemusika uye kugadzirisa maitiro avo zvinoenderana.
3.2.2 Maitiro ekufanotaura
Maitiro ekufungidzira anobvumira traders kufanotaura mafambiro emitengo yeramangwana zvichibva pane zvakaitika kare. Maviri emhando dzinonyanya kushandiswa mukutengesa kwehuwandu iARIMA (Auto-Regressive Integrated Moving Average) uye GARCH (Generalized Autoregressive Conditional Heteroskedasticity).
- ARIMA inoshandiswa kufanotaura nguva yakatevedzana data inoratidza mapatani e autocorrelation. Iyi modhi inonyanya kushanda pakuita fungidziro yemutengo wenguva pfupi kubva pane data yapfuura. Nekuona uye kuenzanisira hukama pakati pezvakarebesa nguva, ARIMA inogona kupa traders ine chishandiso chine simba chekutarisira mafambiro emitengo mune ramangwana.
- GARCH inowanzoshandiswa kuenzanisira kukanganisa mumisika yemari. Sezvo kusagadzikana kuri chinhu chakakosha pakusarudza mitengo uye kubata njodzi, GARCH yakakosha kune traders vanoda kufanotaura nguva dzepamusoro kana kuderera kwekusagadzikana uye kugadzirisa maitiro avo zvinoenderana.
Ose ari maviri ARIMA uye GARCH anobvumira traders kukudziridza fungidziro yakanyatsojeka uye yakavimbika, ichivabatsira kuita sarudzo dzekutengesa dzine ruzivo.
3.2.3 Technical Analysis Indicators
Technical ongororo zviratidzi zviturusi zvinoshandiswa kuongorora mitengo mapatani uye kufanotaura mafambiro emitengo yeramangwana. Izvi zviratidzo zvinowanzotorwa kubva munhoroondo yenguva yakatevedzana data uye chinhu chakakosha chehuwandu hwekutengesa nzira.
Zvimwe zvakajairika tekinoroji yekuongorora zviratidzo zvinosanganisira:
- Kufamba zvishoma, iyo inotsvedza data yemutengo kuona kutungamira kwemaitiro pane yakatarwa nguva.
- Hama Strength Index (RSI), iyo inoyera kukurumidza uye kuchinja kwekufamba kwemitengo kuona overbought kana oversold mamiriro.
- Bollinger Bands, iyo inoshandisa misiyano yakajairwa yakatenderedza avhareji inofamba kuti itsanangure mitengo yemitengo uye inogona kubuda.
Nekubatanidza zviratidzo izvi mumhando dzavo, huwandu traders inogona kugadzira nzira dzinotora advantage zvemafambiro emusika, tichimhanya, uye mamwe maitiro emitengo.
| mamiriro | Tsanangudzo |
|---|---|
| Zvichida Dzidziso | Anobatsira traders ongorora mukana wezvakasiyana mhedzisiro uye kuverengera zvinotarisirwa kudzoka. |
| Hypothesis Kwayedza | Inotarisa kana kucherechedzwa kwekutengesa kuita kwakakosha zvakanyanya kana mhedzisiro yemukana. |
| Kudzora Kuongorora | Models hukama pakati pemitengo yeasset uye zvimwe zvinosiyana kuzivisa sarudzo dzekutengesa. |
| Statistical Distributions | Inoshandiswa kuenzanisa mukana wekufamba kwemitengo yeasset uye kugadzirisa njodzi dzemuswe. |
| Nguva Series Zvikamu | Inoongorora mafambiro, mwaka, cyclical mapatani, uye zvisizvo mudhata remutengo weasset. |
| Forecasting Techniques | ARIMA neGARCH modhi dzinoshandiswa kufanotaura mafambiro emitengo uye kusagadzikana kwemusika. |
| Technical Analysis Indicators | Zvishandiso zvakaita sekufamba mavhareji uye RSI zvinobatsira kuona mafambiro, kukurumidza, uye mamiriro emusika. |
4. Programming nokuda Quantitative Trading
Kuronga hunyanzvi hwakakosha hwehuwandu traders, sezvo zvichivagonesa kugadziridza nzira dzavo dzekutengesa, kuongorora mahombe dataset, uye kuita backtesting. Muchikamu chino, isu tichaongorora inonyanya kufarirwa mitauro yehurongwa inoshandiswa mukutengesa kuwanda, maraibhurari akakosha uye maturusi, backtesting masisitimu, uye data masosi ayo akawanda. traders kuvimba.
4.1 Yakakurumbira Programming Mitauro
Zvakawanda traders anofanirwa kuve nehunyanzvi mumutauro mumwe chete wekuronga kuti vanyatso gadzira uye nekuita hurongwa hwavo. Mitatu yemitauro inonyanya kushandiswa munzvimbo ino iPython, R, uye C++.
Python inoonekwa zvakanyanya semutauro unonyanya kufarirwa wekutengesa kwehuwandu nekuda kwekureruka kwekushandisa uye yakakura raibhurari. tsigira. Kuchinjika kwePython uye kureruka kunoita kuti ive yakanakira kugadzira maalgorithms ekutengesa, kuita ongororo yedata, uye kubatanidza nemaAPI data data. Vatengesi vanogona kunyora zviri nyore zvinyorwa kuita otomatiki maitiro, kuongorora data, uye kugadzira backtesting modhi vachishandisa Python yakapfuma ecosystem yemaraibhurari.
R mumwe mutauro une simba unofarirwa neuwandu traders, kunyanya kuongororwa kwenhamba uye kuona data. R kugona kubata kwakaomarara manhamba ekuverenga kunoita kuti ive sarudzo yakakurumbira pakati traders vanovimba nenhamba dzemhando dzemaitiro avo. Pamusoro pezvo, R ine rutsigiro rwakasimba rwekuwongorora nguva yekutevedzana uye zvemari data manipulation, ayo akakosha pakuvaka akasimba ekutengesa modhi.
C++ mutauro unozivikanwa nekumhanya uye kushanda nesimba, zvichiita kuti uve wakanakira kutengeserana kwepamusoro-soro uko nguva yekuuraya yakakosha. Kunyange zvazvo zvakanyanya kuoma dzidza kupfuura Python kana R, C ++ inobvumira traders kukwirisa maalgorithms avo ekuita, izvo zvakakosha mumisika umo mamilliseconds anogona kuita mutsauko pakati pepurofiti nekurasikirwa. High-frequency traders inowanzovimba neC ++ kugadzira latency-sensitive masisitimu anogona kugadzirisa mavhoriyamu makuru e data munguva chaiyo.
4.2 Akakosha Maraibhurari uye Zvishandiso
Zvakawanda traders inowedzera huwandu hwakawanda hwemaraibhurari uye maturusi ekugadzira maitiro avo uye kuongorora data. Kuzivikanwa kwePython kunotsigirwa nekuunganidzwa kwayo kwakakura kwemaraibhurari akagadzirirwa chaizvo kuongororwa kwemari uye kutengesa kwehuwandu.
NumPy iraibhurari yakakosha muPython yenhamba komputa. Inopa tsigiro yeakakura akawanda-dimensional arrays uye matrices, pamwe nehupamhi hwemabasa emasvomhu. NumPy's inoshanda array mashandiro anoita kuti ive yakakosha chishandiso chekubata mahombe dataset uye kuita maverengero akaomarara mukutengesa algorithms.
Pandas imwe yaikosha raibhurari muPython, yakanyatsogadzirirwa kugadzirisa data uye kuongorora. Inopa zvimiro zve data seDataFrames, inobvumira traders kushandisa nyore, kusefa, uye kuongorora data yenguva. Pandas inopawo mabasa ekuverenga data kubva kune akasiyana faira mafomati uye APIs, zvichiita kuti zvive nyore kubatanidza data rezvemari mumhando dzekutengesa.
SciPy iraibhurari yemakomputa yesainzi inozadzisa NumPy nekupa humwe mashandiro ekugadzirisa, kusanganisa, uye kuongorora kwenhamba. SciPy's suite yezvishandiso inoshandiswa zvakanyanya muhuwandu hwemari kuita epamusoro masvomhu computations, senge chiratidzo chekugadzirisa, izvo zvinogona kukosha pakuona mikana yekutengesa.
Matplotlib iraibhurari yekuronga inobvumira traders kuona data uye mhedzisiro yemaitiro avo. Kuona data kuburikidza nemachati uye magirafu kunobatsira traders spot maitiro, ongorora mashandiro emamodheru avo, uye gadzirisa pazvinenge zvichidikanwa.
4.3 Backtesting Frameworks
Backtesting inzira yakaoma mukutengesa kwehuwandu, sezvainobvumira traders kuongorora maitiro avo vachishandisa nhoroondo data vasati vaashandisa mumisika mhenyu. Zviitiko zvinoverengeka zvekudzosera kumashure zvakagadziridzwa kuti zvigadzirise maitiro aya, zvichipa maturusi anodiwa kutevedzera trades uye kuongorora kushanda.
Zipline iPython-based backtesting raibhurari inozivikanwa pakati pehuwandu traders nokuda kwekushanduka kwayo uye nyore kushandisa. Zipline inopa yakavakirwa-mukati chiitiko-inofambiswa sisitimu inotevedzera chaiyo yekutengesa nharaunda, inobvumira traders kuti vaedze maitiro avo vachipesana nenhoroondo data. Inosanganisirwawo nedata masosi seQuandl, zvichiita kuti zvive nyore kusanganisa emhando yepamusoro data rezvemari mune zvekumashure.
QuantConnect ipuratifomu-yakavakirwa gore inopa backtesting uye kurarama kwekutengesa kugona. Iyo inotsigira akawanda makirasi easset uye inopa mukana kune nhoroondo yemusika data, inogonesa traders kuti vaedze maitiro avo mumisika yakasiyana siyana. QuantConnect's platform inobvumira traders kuvandudza mazano avo vachishandisa Python kana C #, zvichiita kuti iwanike traders vanoda chero mutauro.
shuretrader imwe Python-based framework yakagadzirirwa kudzosera kumashure uye kutengesa. Inopa yakasimba uye inochinjika chikuva che traders kuti vaedze marongero avo, kukwidziridza paramita, uye kuongorora mashandiro. Backtrader inotsigira akawanda data masosi uye inogona kushandiswa kune ese ari maviri backtesting uye rarama kutengesa, zvichiita kuti ive chishandiso chinogoneka chehuwandu. traders.
4.4 Dhata Sosi
Dhata ndiro ropa rekutengesa kwehuwandu, uye kuwana kune yemhando yepamusoro data kwakakosha pakuvandudza, kuyedza, uye kunatsa nzira dzekutengesa. Vanoverengeka vanopa data vanopa iyo data yemari iyo traders vanovimba ne backtesting uye kurarama kutengeserana.
Bloomberg ndomumwe wevanonyanya kuzivikanwa vanopa data rezvemari. Iyo terminal inopa chaiyo-nguva yemusika data, nhau, analytics, uye tsvakurudzo. Bloomberg kuvharika kwakazara kwemisika yemari yepasi rose inoita kuti ive chinhu chakakosha sosi yehuwandu. traders vanoda data panguva uye chaiyo.
Reuters inopa imwe yakakosha sosi yedata rezvemari, inopa chaiyo-nguva yemusika nhau, data feeds, uye analytics. Reuters' data inoshandiswa zvakanyanya nemasangano traders uye rusvingo mari yekuzivisa zvisarudzo zvavo zvekutengesa uye mazano.
Quandl ipuratifomu yakakurumbira inopa mukana kune akasiyana siyana emari uye ehupfumi dataset. Inopa ese emahara uye premium data pamakirasi akasiyana easset, kusanganisira equities, Zvigadzirwa, uye macroeconomic zviratidzo. Quandl's API inobvumira traders kuti ibatanidze data rayo nyore muhuwandu hwemhando dzekuongorora uye backtesting.
| Kuonekwa | Tsanangudzo |
|---|---|
| Yakakurumbira Programming Mitauro | Python yekuchinjika uye nyore kushandisa; R yekuongorora nhamba; C ++ ye-high-frequency trading performance. |
| Akakosha Maraibhurari uye Zvishandiso | NumPy, Pandas, SciPy yenhamba uye data analysis; Matplotlib yekuona data. |
| Backtesting Frameworks | Zipline, QuantConnect, uye Kumashuretrader mapuratifomu akakurumbira ekutevedzera nzira dzekutengesa nenhoroondo data. |
| Data Sources | Bloomberg, Reuters, uye Quandl inopa iyo yepamusoro-mhando yemari data inodiwa pakutengesa kwehuwandu. |
5. Yakakurumbira Quantitative Trading Strategies
Nzira dzekutengesa dzehuwandu dzakagadzirirwa kukwidziridza kusaita zvakanaka kana mapatani mumisika yezvemari nekushandisa mhando dzinofambiswa nedata. Aya marongero anogadzirwa achishandisa masvomhu, manhamba, uye algorithmic maturusi, uye anoitwa nemazvo kuburikidza neautomation. Muchikamu chino, tichaongorora mamwe eanonyanya kushandiswa ehuwandu hwekutengesa nzira, kusanganisira kureva-reversion, kukurumidza, arbitrage, uye muchina kudzidza-kwakavakirwa mazano.
5.1 Mean-Reversion Strategies
Mean-reversion strategy yakavakirwa pafungidziro yekuti mitengo yeasset inozopedzisira yadzokera kuavhareji yavo yenhoroondo kana kureva nekufamba kwenguva. Nenzira iyi, traders kutsvaga kuwana purofiti kubva mukutsauswa kwemitengo kubva paavhareji, vachibhejera kuti kutsauka uku ndekwenguva pfupi uye vachazvigadzirisa.
Pfungwa yakakosha kuseri kwezvinoreva-reversion nzira ndeyekuti kana mutengo weasset waenda kure zvakanyanya kubva pane zvazvinoreva, unozopedzisira wadzokera kune izvo zvinoreva. Izvi zvinogadzira mikana ye traders kutenga zvinhu zvisingakosheswi (pasi pezvinoreva) uye kutengesa izvo zvakawandisa (pamusoro pezvinoreva). Iyo nzira inotsamira pakuziva kana mitengo yatsauka zvakanyanya kubva pazvinoreva uyezve kuita trades kushandisa kusashanda kwechinguvana uku.
Muenzaniso wakajairwa wenzira yekudzosera kumashure kutengeserana kwevaviri, kunosanganisira kudoma zvinhu zviviri zvine chekuita nenhoroondo uye kutengesa mutsauko mumitengo yazvo. Kana mutengo wechimwe chinhu ukatsauka kubva kune imwe, iyo trader inotora zvinzvimbo muzvinhu zvese zviri zviviri, ichitarisira kuti mitengo yavo ichasangana zvakare. Iri zano rinofungidzira kuti hukama hwenhoroondo pakati pezvinhu zviviri izvi hucharamba hwakasimba.
Imwe mhando yezvinoreva-reversion zano ndeye statistical arbitrage, kupi traders vanoshandisa manhamba ezviverengero kuona mispricings mumhando dzakasiyana-siyana dzezvinhu. Nekuongorora nhoroondo dzenhoroondo, traders inogona kuona zvinhu zvinotarisirwa kudzoka kune zvazvinoreva uye kuita trades saizvozvo. Iri zano rinowanzo shandiswa pamativi makuru makuru, achibvumira traders kuwana purofiti kubva mukusashanda kudiki pamusika.
5.2 Momentum Strategies
Mazano ekusimudzira anobva papfungwa yekuti midziyo yakaita zvakanaka munguva yakapfuura icharamba ichiita izvi munguva pfupi iri kutevera, uye iyo isina kunyatsoshanda icharamba ichiderera. Aya mazano anotora advantage yemaitiro emusika nekubheja kuti mafambiro emitengo mune imwe nzira acharamba aripo kwenguva yakati.
Trend inotevera inzira yakajairika yekukurumidza kutengesa, kupi traders kutsvaga kutora kukwira kana kudzika kweasset nekutevera maitiro akaiswa. Vatengesi vanoshandisa zano iri vanovavarira kuona mafambiro ekutanga uye kubata zvinzvimbo kudzamara maitiro aratidza zviratidzo zvekudzokera kumashure. Kufamba maavhareji, kunyanya kufambisa avhareji macrossovers, anowanzo shandiswa mumatanho ekukurumidza kuratidza kutanga kana kupera kwemaitiro. Semuenzaniso, kana avhareji yenguva pfupi inofamba ichiyambuka pamusoro peavhareji yenguva refu inofamba, inogona kuratidza kutanga kwemaitiro ekukwira, zvichikurudzira. traders kutenga.
Breakout mazano ndeimwe mhando yekukurumidza kutengesa. Aya mazano anosanganisira kuziva mazinga emitengo apo asset inoputika kubva pane yakatsanangurwa, zvichiratidza kuenderera mberi kwemaitiro ayo aripo. Vatengesi vanotsvaga zvinhu zviri kupaza nekiyi yekupokana kana nhanho dzekutsigira uye kupinda munzvimbo dziri munzira yekubuda. Kutenda pano ndekwekuti iyo asset icharamba ichifamba munzira yekubuda, inokurudzirwa nekusimba kwemusika.
Maitiro eMomentum anowanzo shanda nemazvo mumisika inofamba asi anogona kunetseka panguva yekubatanidzwa kana market reversals. Nekuda kweizvozvo, traders inoda kunyatso tarisa zvinzvimbo zvavo uye kushandisa maturusi ekudzora njodzi kuti vazvidzivirire kubva kukuchinja kwakangoerekana kwaitika mumamiriro emusika.
5.3 Arbitrage Strategies
Arbitrage mazano akagadzirirwa kushandisa kusiyana kwemitengo pakati pezvinhu zvine hukama mumisika yakasiyana kana mari yekushandisa. Aya marongero anovimba nemusimboti we "kutenga zvakaderera, tengesa zvakakwirira" nekutenga panguva imwe chete nekutengesa asset kana chinhu chine hukama kutora mutsauko wemutengo. Mazano eArbitrage anowanzo kuve ane njodzi yakaderera asi inoda kukurumidza kuuraya uye kuwana akawanda misika kuti ishande.
Statistical arbitrage ndeimwe yeanowanzo mafomu e arbitrage muhuwandu hwekutengesa. Muchirongwa ichi, traders vanoshandisa manhamba ezviverengero kuti vaone kuchengetedzwa kwakashata maererano nehukama hwavo hwekare nezvimwe zvinhu. Nekuita tradeizvo zvinotora mari pane izvi zvisirizvo zvenguva pfupi, traders inogona kuita pundutso kubva mukusangana kwemitengo yeasset kudzokera kuhukama hwavo hwemazuva ese. Iri zano rinowanzo shandiswa kune yakafara tswanda yezvibatiso kuderedza njodzi uye kuwedzera mukana wepurofiti.
Musika microstructure arbitrage imwe nzira yearbitrage inotarisa pakushandisa zvisina basa mukati mekutengesa nzira dzemisika yemari. Vatengesi vanoshandisa zano iri vanoongorora bhidhi-kubvunza kupararira, kuyerera kwehurongwa, uye liquidity yemisika yakasiyana-siyana yekuona mikana apo mitengo yakakanganiswa zvishoma. Nokukurumidza kuita trademunzvimbo dzakasiyana siyana, traders inogona kutora purofiti diki kubva mukusashanda kwenguva pfupi uku.
Nepo nzira dzearbitrage dzichiwanzoonekwa sedzisina njodzi nekuda kwekuvimba kwavo nekusiyana kwemitengo, dzinoda tekinoroji yepamusoro uye nekukurumidza kuti ishande. Nekukwira kwekutengesa kwepamusoro-soro, mikana yakawanda yearbitrage inokurumidza kugadziriswa, zvichiita kuti zviwedzere kunetsa kushandisa mazano aya pasina kushandiswa kwemaitiro akaoma.
5.4 Machine Kudzidza Strategies
Mazano ekudzidza emuchina anomiririra kuchekwa kwekutengesa kwehuwandu, uko maalgorithms anogona "kudzidza" kubva kune nyowani data uye kugadzirisa mamodheru avo kuti ashanduke mamiriro emusika. Makina ekudzidza emuchina anonyanya kukosha mumisika yakaoma, ine simba apo mhando dzechinyakare dzinogona kutadza kutora maitiro ari kubuda.
Kusimbisa kudzidza ibazi rekudzidza muchina rinonyanya kubatsira pakugadzira nzira dzekutengesa. Nenzira iyi, maalgorithms anodzidza nekudyidzana nenzvimbo yemusika uye kugamuchira mhinduro muchimiro chemibairo kana zvirango. Nekufamba kwenguva, iyo algorithm inogadzirisa zano rayo kuti riwedzere mibairo, senge purofiti, uku ichideredza zvirango, sekurasikirwa. Mazano ekusimbisa ekudzidza anowanzo shandiswa mumasisitimu anochinja-chinja anoda kugadzirisa kuchinja mamiriro emusika munguva chaiyo.
Kudzidza kwakadzama chimwe chishandiso chine simba mukudzidza muchina, chinosanganisira kushandiswa kweartificial neural network kuenzanisira hukama hwakaoma mune data rezvemari. Nekuongorora huwandu hwakakura hwenhoroondo yemusika data, yakadzama yekudzidza algorithms inogona kuona mapatani uye mafambiro asiri kuoneka kumunhu. traders. Aya mamodheru anogona kunyanya kushanda mukufanotaura mafambiro emutengo wenguva pfupi uye kuona mikana yekutengeserana inobatsira.
Nzira dzekudzidza dzemuchina dzinoda simba rakawanda rekuverengera uye dhatabhesi hombe kudzidzisa mamodheru. Nekudaro, kana mamodheru aya akagadzirwa, anogona kupa traders ine mupendero wemakwikwi nekuvagonesa kuona nekushandisa mapatani asingaonekwe nyore nenzira dzechinyakare.
| Strategy Type | Tsanangudzo |
|---|---|
| Mean-Reversion Strategies | Tarisa pakushandisa kutsauswa kwemitengo kubva kumavhareji ezvakaitika kare; mienzaniso yakajairika inosanganisira vaviri vaviri kutengesa uye statistical arbitrage. |
| Momentum Strategies | Shandisa mari pamaitiro nekubheja kuti mafambiro emitengo mune imwe nzira anoenderera; sanganisira maitiro ekutevera uye mazano ekuputsa. |
| Arbitrage Strategies | Shandisa kusiyana kwemitengo pakati pezvinhu zvine hukama; marudzi akajairika anosanganisira statistical arbitrage uye musika microstructure arbitrage. |
| Machine Kudzidza Strategies | Shandisa maalgorithms anogadzirisa uye kudzidza kubva kutsva data; Kusimbisa kudzidza uye kudzidza kwakadzama inzira dzakakurumbira. |
6. Zvishandiso Zvekudzidza
Quantitative trading inzvimbo yakaoma inoda kuenderera mberi nekudzidza uye kugadzirisa. Pane zvakawanda zvekushandisa zviripo traders vanoda kudzamisa kunzwisisa kwavo nekuvandudza hunyanzvi hwavo. Kubva kumabhuku kuenda kumakosi epamhepo, zviwanikwa zvemahara, uye zvitupa, traders kuwana hupfumi hweruzivo rwekuvabatsira kugona kutengesa kwehuwandu.
6.1 Mabhuku Akanakisa eKutengesa Kwehuwandu
Mabhuku anopa hwaro hwakasimba hwe traders vanoda kuongorora huwandu hwekutengesa mune zvakadzama. Vanopa tsananguro dzakadzama dzemazano ekutengesa, masvomhu modhi, uye anoshanda maapplication anogona kubatsira zvakanyanya kune vese vanotanga uye vane ruzivo. traders.
Rimwe remabhuku anokurudzirwa zvakanyanya mumunda uyu ndere "Algorithmic Kutengeserana: Kuhwina Strategies uye Mafungiro Avo" naErnest Chan. Iri bhuku rinopa gwara rinoshanda kune algorithmic yekutengesa marongero uye rinopa manzwisisiro ekuti sei traders vanogona kugadzira uye kuita yavo yehuwandu nzira. Chan anotsanangura chikonzero chemaitiro uye anopa mienzaniso chaiyo yepasirese iyo traders vanogona kunyorera mune yavo yekutengeserana masisitimu.
Rimwe bhuku rinokosha ndere "Kutengesa Kwakawanda: Maitiro Ekuvaka uye Kumhanyisa Inobudirira Algorithmic Kutengesa Bhizinesi" naDavid Weiss. Iri bhuku rinotarisa pane zvekushanda zvinhu zvekumhanyisa bhizinesi rekutengesa rakawanda, kusanganisira matambudziko ekuvaka algorithms, kutsvaga data, uye kutonga njodzi. Zvinonyanya kukosha kune traders vanoda kuenderera mberi kwekuvandudza nzira uye vanonzwisisa maitiro ekuyera masystem avo ekutengesa zvinobudirira.
Mabhuku maviri aya anosimbisa kukosha kwekudzosera kumashure, manejimendi enjodzi, uye kuongororwa kwedata, kupa traders nekunzwisisa kwakazara kwezvinoda kuti ubudirire mukutengesa kwehuwandu.
6.2 Makosi epamhepo
Kune avo vanosarudza yakawedzera kurongeka yekudzidza nharaunda, online makosi inzira yakanaka yekuwana hunyanzvi hunoshanda mukutengesa kwehuwandu. Mapuratifomu akaita seCoursera, edX, uye Udemy anopa akawanda akasiyana makosi anovhara misoro senge algorithmic kutengeserana, kuongororwa kwedata rezvemari, uye kudzidza muchina kwemari.
Coursera, kunyanya, inopa makosi kubva kumayunivhesiti epamusoro uye masangano emari, achibvumira traders kudzidza kubva kunyanzvi dzeindasitiri uye mapurofesa. Mazhinji emakosi aya akagadzirirwa vese vanotanga uye vadzidzi vepamberi, vachipa kuchinjika maererano nekumhanya nekudzika. edX inopawo makosi muhuwandu hwemari uye algorithmic kutengeserana, kazhinji yakatarisana neiyo masvomhu uye tekinoroji maficha emunda.
Udemy inozivikanwa nekupa dzakasiyana siyana makosi, kusanganisira chaiwo misoro senge Python chirongwa chemari, backtesting nzira dzekutengesa, uye muchina kudzidza mukutengesa. Mazhinji emakosi eUdemy akagadzirirwa kuve-maoko, achibvumira vadzidzi kuvaka uye kuyedza yavo yekutengeserana algorithms sezvavanofambira kuburikidza nezvinhu.
6.3 Zvishandiso Zvemahara
For traders vanoda kuwedzera ruzivo rwavo pasina kuunza mutengo wakakura, kune akati wandei emahara zviwanikwa zviripo. Mablogiki, maforamu, uye nzira dzeYouTube dzakatsaurirwa kuhuwandu hwekutengesa zvinopa hupfumi hweruzivo pamazano akasiyana siyana, maturusi, uye matekiniki.
MaBlogs akadai seQuantocracy curate zvemukati kubva kutenderera webhu, achipa traders ane zvinyorwa, mapepa ekutsvagisa, uye zvidzidzo zvehuwandu hwekutengesa misoro. Aya mablogiki inzira yakanaka yekugara yakagadziridzwa pane zvichangoburwa zviri kuitika mumunda uye kuwana mazano matsva nemidziyo.
Maforamu akaita seQuantNet uye Elite Trader anobvumira traders kubatana nevamwe munharaunda, kugovana mazano, uye kukurukura zvakasiyana-siyana zvekutengesa kwehuwandu. Maforamu aya anonyanya kubatsira traders vanoda kuwana mhinduro pamazano avo kana kugadzirisa nyaya dzetekinoroji dzine chekuita nekuronga uye kuongorora data.
Nzira dzeYouTube dzinopawo zvidzidzo zvakakosha pakutengesa kwehuwandu, iine dzimwe nzira dzinotarisa pamitauro yekuronga sePython neR, nepo vamwe vachiongorora nzira dzekutengesa uye kuongororwa kwemusika. Izvi zviwanikwa zvinopa nzira inodyidzana yekudzidza, se traders inogona kutevera pamwe nekuratidzira kwekodhi uye tsananguro dzehurongwa.
6.4 Certification
Zvitupa zvinopa kucherechedzwa kuri pamutemo kwe trader's hunyanzvi uye inogona kuve yakakosha kune avo vari kutsvaga kusimudzira mabasa avo muhuwandu hwekutengesa kana mari. Zvitupa zvakati wandei zvinonyanya kukosha kune huwandu traders.
Iyo Chartered Financial Analyst (CFA) certification ndeimwe yemazita anoremekedzwa muindasitiri yezvemari. Kunyangwe isiri yakanangana nekutengesa kwehuwandu, chirongwa cheCFA chinovhara misoro yakakosha senge portfolio manejimendi, kuongororwa kwemari, uye manejimendi enjodzi, izvo zvese zvakakosha pahuwandu. traders.
Iyo Certified Quantitative Analyst (CQA) chitupa chakanyanya hunyanzvi uye chinotarisa zvakanyanya pahuwandu hwemari. Chirongwa cheCQA chinovhara nzvimbo dzakaita sekuongorora kwenhamba, kuwanda kwekuenzanisa, uye algorithmic kutengeserana, zvichiita kuti ive yakakosha kune. traders vanoda kuratidza hunyanzvi hwavo mundima iyi.
Izvi zvitupa hazvingowedzere a trader's kuvimbika asi zvakare inopa yakarongeka nzira dzekudzidza dzinovhara zvese zvedzidziso uye zvinoshanda zvekutengesa kwehuwandu.
| Kudzidza Resource | Tsanangudzo |
|---|---|
| Books | "Algorithmic Trading" naErnest Chan uye "Quantitative Trading" naDavid Weiss inopa ruzivo rwakakwana mumazano uye mashandiro ebhizinesi. |
| Online Courses | Mapuratifomu akaita seCoursera, edX, uye Udemy anopa makosi akarongwa pamisoro kubva kualgorithmic kutengeserana kusvika muchina kudzidza kune mari. |
| Free Resources | Mablogiki, maforamu, uye YouTube chiteshi zvinopa zvemahara zvemukati, tutorials, uye nhaurirano dzenharaunda pamusoro pehuwandu hwekutengesa nzira uye matekiniki. |
| Certifications | CFA neCQA zvinozivikanwa zvitupa zvinoratidza hunyanzvi mune zvemari uye kuwanda kwekutengesa. |
mhedziso
Quantitative yekutengeserana inomiririra yakanyanyisa uye inofambiswa nedata nzira yekufambisa misika yemari. Iyo inobatanidza nyika dzemari, masvomhu, uye zvirongwa, zvinogonesa trader kuita zvisarudzo zvine ruzivo, zvine chinangwa izvo zvisina kurerekera pamoyo. Hwaro hwekutengesa kwehuwandu huri mukukwanisa kwayo kuongorora huwandu hwakawanda hwe data, kushandisa epamberi masvomhu modhi, uye kuita. trades otomatiki kuburikidza nealgorithms.
Mugwaro rese iri, takaongorora zvakakosha zvekutengesa kwehuwandu, kutanga nekunzwisisa kwakajeka kweiyo yakakosha pfungwa. Kubva paalgorithmic kutengesa uye backtesting kune njodzi manejimendi uye kuongororwa kwedata, zvinhu izvi zvinoumba zvivharo zvekuvaka zvakabudirira zvehuwandu hwekutengesa nzira. Isu takakurukurawo kukosha kwekuve nekunzwisisa kwakasimba kwehwaro hwemasvomhu hwehuwandu hwekutengesa, senge probability theory, regression analysis, uye time series wongororo, izvo zvese zvinobatsira kune dzimwe nzira dzakarurama uye dzine pundutso.
Kuronga hunyanzvi hunyanzvi hunosimbisa kuvandudzwa kwehuwandu hwekutengesa masisitimu, nemitauro yakaita sePython, R, uye C ++ yakakosha pakukodha algorithms uye kuongorora data. Iko kushandiswa kwemaraibhurari akakosha, backtesting masisitimu, uye akavimbika data masosi anovimbisa izvozvo traders inogona kuvaka yakasimba uye inoshanda masisitimu. Uyezve, takaongorora nzira dzinonyanya kufarirwa dzehuwandu hwekutengesa, kusanganisira kureva-kudzosera, kukurumidza, arbitrage, uye nzira dzekudzidza-muchina, imwe neimwe ichipa nzira dzakasiyana dzekushandisa kusashanda kwemusika.
Zvishandiso zvekudzidza zvakakoshawo kune chero trader kutarisa kugona munda wehuwandu hwekutengesa. Mabhuku, makosi epamhepo, zviwanikwa zvemahara, uye zvitupa zvinopa yakakwana mikana yedzidzo ye traders pamatanho ese. Sezvo mamiriro emari ari kuramba achishanduka, kugara uchienderana nezviri kuitika mukutengesa kwehuwandu kwakakosha kuti ubudirire.
Mukupedzisa, kutengeserana kwehuwandu hakusi kungotevera seti yemitemo kana kuvimba nemusika intuition. Iri nezve reveraging data, ongororo yenhamba, uye otomatiki kugadzira mazano ari ese ari scale uye anogona kuchinjika. Nekugona misimboti uye matekiniki anotsanangurwa mugwaro iri, traders vanogona kuzvimisa kuti vatore advantage yehukuru hwakakura hunopihwa nekutengesa kwehuwandu mumisika yemazuva ano inomhanya-mhanya.
