Algo Trading
Enjoy automated trading experience with our State-of-Art platform
Algo Trading Software Features
Portfolio performance analysis using NAV, Index, CAGR for a particular period or since inception.Detailed analysis using tools such as Decision Analysis and Growth.
In App short Term recommendation based on CANSLIM methodolgy
FnO calls by our inhouse expert with more than 70% success ratio
Extensive research on 4000+ companies Get the latest news and reports from analysts on 4000+ companies
FnO calls by our inhouse expert with more than 70% success ratio
FnO calls by our inhouse expert with more than 70% success ratio

Partners
Process of Algo trading
Conception
Ideate and Conceptualize your trading strategy
Access
Define Rules, Risks and Returns
Build Strategy
Use Ready Made or Custom strategy
Strategy Testing
BackTest and LiveTest your strategy
Approval
Fine tune your strategy as per testing reports
FAQs
Also known as ‘Black-box trading,’ Algorithmic trading involves the use of computer programs to place a trade based on predefined rules and principles. The computer program uses a set of instructions that helps make trading decisions and earns profits at a pace that would be difficult for a human trader to achieve. Algorithmic trading, in addition to giving profit opportunities for traders, makes markets more liquid and trading more systematic by eliminating the effect of human emotions on trading.
Some of the advantages of algorithmic trading include:
1. Rule-based decision making: Traders and investors are frequently influenced by feelings and emotions and tend to trading techniques. Algorithms work to address this problem by guaranteeing that all trades follow a set of rules. Execution of the decisions occurs at the desired levels due to the quick and precise outcomes of computer programs.
2. Reduce market impact: Transaction costs are lower, and the predetermined rules help make automated checks on several market situations simultaneously. A trading algorithm can also purchase shares and check immediately to see if the transaction has influenced the market price.
3. Minimize human fallacy: As algorithmic trading works based on predefined instructions, there is less risk of making mistakes while placing transactions. This lowers the possibility of human traders making mistakes as a result of emotional or psychological factors.
Institutional investors and large brokerage firms mostly utilize algorithmic trading to reduce trading expenses. Algorithmic trading is especially helpful for high order sizes, accounting for up to 10% of global trading activity. Algorithmic trading has gained popularity among both retail and institutional traders in the 21st century. It is popular among investment banks, pension funds, mutual funds, and hedge funds that need to stretch out the execution of a larger order or execute deals that are too quick for human traders to react to. Other institutions that use algorithmic trading include: Investment funds
>Pension funds
>Credit unions
>Investment banks
>Insurance companies
>Trusts
>Prime brokers
Pairs trading: Also known as pair trading, it is a market-neutral technique that allows traders to benefit from short-term differences in the relative value of close substitutes. The law of one price cannot ensure price convergence in pairs trading. This especially applies while using the technique on individual equities. Arbitrage: This approach is used by institutional investors who want to profit from small market price differences when a security’s market price trades on two different exchanges. Three criteria must be satisfied for arbitrage to take place:
First, on all markets, similar assets should not trade at the same price.
Second, two assets with the same cash flows should not be purchased or sold simultaneously.
Finally, an asset with a known future charge should not be traded using that pricing.
Delta-neutral strategies: Delta-neutral refers to a portfolio of linked financial assets in which the portfolio value is unaffected by minor changes in the underlying security’s value. The positive and negative delta components of such a portfolio are generally offset, resulting in the portfolio’s value being relatively insensitive to changes in the value of the underlying investment.
Mean Reversion: Mean reversion is a mathematical approach for investing in stocks that may also be applied to other activities. It is the process of determining a stock’s trading range and then figuring the average price using analytical approaches pertaining to assets, earnings, and other factors.
Trend following: It is one of the most widely utilized algorithm-based trading methods. The goal of this strategy is to uncover patterns employed in the purchasing and selling process.
Scalping: This method is distinct from others. It is determined by the difference in bid and the security price. This approach will need a lot of money to deliver the expected outcomes. As a result of its complexity, it is handled by professionals. If you are new to investing, stay away from this approach until you have mastered the fundamentals of trade strategies.