Algorithmic Trading

IntroBooks
αžŸαŸ€αžœαž—αŸ…β€‹αž’αŸαž‘αž·αž…αžαŸ’αžšαžΌαž“αž·αž…
28
αž‘αŸ†αž–αŸαžš
αž˜αžΆαž“αžŸαž·αž‘αŸ’αž’αž·
αž€αžΆαžšαžœαžΆαž™αžαž˜αŸ’αž›αŸƒ αž“αž·αž„αž˜αžαž·αžœαžΆαž™αžαž˜αŸ’αž›αŸƒαž˜αž·αž“αžαŸ’αžšαžΌαžœαž”αžΆαž“αž•αŸ’αž‘αŸ€αž„αž•αŸ’αž‘αžΆαžαŸ‹αž‘αŸ αžŸαŸ’αžœαŸ‚αž„αž™αž›αŸ‹αž”αž“αŸ’αžαŸ‚αž˜

αž’αŸ†αž–αžΈαžŸαŸ€αžœαž—αŸ…β€‹αž’αŸαž‘αž·αž…αžαŸ’αžšαžΌαž“αž·αž€αž“αŸαŸ‡

Algorithmic trading is an exchange mechanism where computers make choices about what to buy and sell in the money markets. The purpose of algorithmic trading would be to either make money by buying lower and selling higher or to minimize transaction costs by effectively buying or selling large volumes of financial commodities. Depending on those guidelines, the computer determines when and how much to buy and sell. And these norms are designed by manual efforts. Algorithmic Trading typically involves understanding of the financial marketing domain, programming, and knowledge related to data sciences.

Algorithmic trading can be broken down into two segments:

*The revelation of market inefficiencies: People are looking in the markets for something unfair that they can leverage. To illustrate, if two exchanges value a similar financial product differently, there may be a variance.

*People devise a plan to exploit the business incompetence they have detected. It entails determining the ideal moment to buy and sell, the exact quantity to buy and sell, and how to end the trading operations.


αžœαžΆαž™αžαž˜αŸ’αž›αŸƒαžŸαŸ€αžœαž—αŸ…β€‹αž’αŸαž‘αž·αž…αžαŸ’αžšαžΌαž“αž·αž€αž“αŸαŸ‡

αž”αŸ’αžšαžΆαž”αŸ‹αž™αžΎαž„αž’αŸ†αž–αžΈαž€αžΆαžšαž™αž›αŸ‹αžƒαžΎαž‰αžšαž”αžŸαŸ‹αž’αŸ’αž“αž€αŸ”

αž’αžΆαž“β€‹αž–αŸαžαŸŒαž˜αžΆαž“

αž‘αžΌαžšαžŸαž–αŸ’αž‘αž†αŸ’αž›αžΆαžαžœαŸƒ αž“αž·αž„β€‹αžαŸαž”αŸ’αž›αŸαž
αžŠαŸ†αž‘αžΎαž„αž€αž˜αŸ’αž˜αžœαž·αž’αžΈ Google Play Books αžŸαž˜αŸ’αžšαžΆαž”αŸ‹ Android αž“αž·αž„ iPad/iPhone αŸ” αžœαžΆβ€‹αž’αŸ’αžœαžΎαžŸαž˜αž€αžΆαž›αž€αž˜αŸ’αž˜β€‹αžŠαŸ„αž™αžŸαŸ’αžœαŸαž™αž”αŸ’αžšαžœαžαŸ’αžαž·αž‡αžΆαž˜αž½αž™β€‹αž‚αžŽαž“αžΈβ€‹αžšαž”αžŸαŸ‹αž’αŸ’αž“αž€β€‹ αž“αž·αž„β€‹αž’αž“αž»αž‰αŸ’αž‰αžΆαžαž±αŸ’αž™β€‹αž’αŸ’αž“αž€αž’αžΆαž“αž–αŸαž›β€‹αž˜αžΆαž“αž’αŸŠαžΈαž“αž’αžΊαžŽαž·αž αž¬αž‚αŸ’αž˜αžΆαž“β€‹αž’αŸŠαžΈαž“αž’αžΊαžŽαž·αžβ€‹αž“αŸ…αž‚αŸ’αžšαž”αŸ‹αž‘αžΈαž€αž“αŸ’αž›αŸ‚αž„αŸ”
αž€αž»αŸ†αž–αŸ’αž™αžΌαž‘αŸαžšβ€‹αž™αž½αžšαžŠαŸƒ αž“αž·αž„αž€αž»αŸ†αž–αŸ’αž™αžΌαž‘αŸαžš
αž’αŸ’αž“αž€αž’αžΆαž…αžŸαŸ’αžŠαžΆαž”αŸ‹αžŸαŸ€αžœαž—αŸ…αž‡αžΆαžŸαŸ†αž‘αŸαž„αžŠαŸ‚αž›αž”αžΆαž“αž‘αž·αž‰αž“αŸ…αž€αŸ’αž“αž»αž„ Google Play αžŠαŸ„αž™αž”αŸ’αžšαžΎαž€αž˜αŸ’αž˜αžœαž·αž’αžΈαžšαž»αž€αžšαž€αžαžΆαž˜αž’αŸŠαžΈαž“αž’αžΊαžŽαž·αžαž€αŸ’αž“αž»αž„αž€αž»αŸ†αž–αŸ’αž™αžΌαž‘αŸαžšαžšαž”αžŸαŸ‹αž’αŸ’αž“αž€αŸ”
eReaders αž“αž·αž„β€‹αž§αž”αž€αžšαžŽαŸβ€‹αž•αŸ’αžŸαŸαž„β€‹αž‘αŸ€αž
αžŠαžΎαž˜αŸ’αž”αžΈαž’αžΆαž“αž“αŸ…αž›αžΎβ€‹αž§αž”αž€αžšαžŽαŸ e-ink αžŠαžΌαž…αž‡αžΆβ€‹αž§αž”αž€αžšαžŽαŸαž’αžΆαž“β€‹αžŸαŸ€αžœαž—αŸ…αž’αŸαž‘αž·αž…αžαŸ’αžšαžΌαž“αž·αž€ Kobo αž’αŸ’αž“αž€αž“αžΉαž„αžαŸ’αžšαžΌαžœβ€‹αž‘αžΆαž‰αž™αž€β€‹αž―αž€αžŸαžΆαžš αž αžΎαž™β€‹αž•αŸ’αž‘αŸαžšαžœαžΆαž‘αŸ…β€‹αž§αž”αž€αžšαžŽαŸβ€‹αžšαž”αžŸαŸ‹αž’αŸ’αž“αž€αŸ” αžŸαžΌαž˜αž’αž“αž»αžœαžαŸ’αžαžαžΆαž˜β€‹αž€αžΆαžšαžŽαŸ‚αž“αžΆαŸ†αž›αž˜αŸ’αž’αž·αžαžšαž”αžŸαŸ‹αž˜αž‡αŸ’αžˆαž˜αžŽαŸ’αžŒαž›αž‡αŸ†αž“αž½αž™ αžŠαžΎαž˜αŸ’αž”αžΈαž•αŸ’αž‘αŸαžšαž―αž€αžŸαžΆαžšβ€‹αž‘αŸ…αž§αž”αž€αžšαžŽαŸαž’αžΆαž“αžŸαŸ€αžœαž—αŸ…β€‹αž’αŸαž‘αž·αž…αžαŸ’αžšαžΌαž“αž·αž€αžŠαŸ‚αž›αžŸαŸ’αž‚αžΆαž›αŸ‹αŸ”