Will the rapid rise in algo trading leave traditional traders behind?

Algo trading is now a ‘prerequisite’ for surviving in tomorrow’s financial markets.

1-thegoodtheba

Financial trading floors are experiencing a huge transition from innovative technologies. It has given traders more powers to do fast execution of trades with discipline in a rapidly changing market scenario by reducing human errors, as computer-programmed software remains unaffected by human psychology.

In today’s era, where more and more traditional traders follow technical charting for their trading calls, an algo trader finds it risky to depend merely on the findings gathered from an examination of charts and, thus, tries to reply on pure arithmetic.

Ten years ago, a financial institution would take a big trade on to its books, and would have a large number of traders try and execute deals in small chunks without moving the market. Today, many big trades are fed into computers running algo programmes, which then execute them automatically in small packets. The biggest advantage of programmed trades is their capability of spotting arbitrage opportunities between prices in split seconds and executing trades to make a profit even before a human trader blinks.

Algos have also been created to trade upon news, using special programmes to scan incoming agency reports for key words relating to, say, a change in interest rates and enact deals based on market responses to similar past events. Furthermore algos can analyse every quote and trade in the stock market, identify liquidity opportunities and turn such information into intelligent trading decisions. Here rules are pre-defined, back-tested and trades are placed at pre-defined levels.

In India, algo trading arrived in the financial markets when Sebi allowed exchange members to offer DMA (direct market access) to their institutional clients. Basically, investment banks and hedge funds with billions of dollars in AUMs (assets under management) are using algo trade to manage their portfolios in a more strategic manner. In India, they account for 35-40 per cent of total turnover on the exchanges.

Algorithmic trading has ushered in a new era for markets, whose benefits are yet to be fully realised. Adapting to this new means of trading can ensure better results. Algo trading is now a ‘prerequisite’ for surviving in tomorrow’s financial markets, because the future of trading and dealing is in automation.

Industry reports suggest global algorithmic trading market size is expected to grow from $11.1 billion in 2019 to $18.8 billion by 2024, expanding at a compound annual growth rate (CAGR) of 11.1 per cent. Although algo trading outperforms traditional styles of trading on many counts, human intervention is still required to some extent for better market making with prudent thoughts to ensure stability in financial markets.

Advertisements

SEMINAR On Greeksoft Algo Software Strategies Awareness

Dear Sir/Madam,

Thank you for your continued support and co-operation for being our valued customer all the time.

We are organizing one day Seminar on G.A.T.S (Greeksoft Automated Trading System) which will  aware you to know more about our Algo Software Strategies .

Seminar on GATS IMG-20190219-WA0004 (2)

We sincerely appreciate your participation in maximum numbers.

For further details, please have a look on the above image.

Workshop on GTT and G.I.T.A.

Dear Sir/Madam,

Thank you for your continued support and co-operation for being our valued customer all the time. We are happy to announce that we have come up with yet another launch of G.T.T (Greeksoft Trend Trader) in reformed manner which will add feather to our existing service.

We are organizing one day workshop on basics on Technical Analysis & G.T.T. which will help you to understand charting techniques in detailed manner. G.T.T. will facilitate trading strategies to code by writing user friendly formulae and printing it on chart. Automated trading can also be done through G.T.T. function which print Buy or Sell signal in Greeksoft software. With that, it will also enhance practical knowledge to understand market by analyzing charting strategies and indicators as mentioned in our template as shown below.

gita pamphlet dark final low size

We sincerely appreciate your participation in maximum numbers.

For further details, please have a look on the above template.

Many Thanks,

Greeksoft Team

Commodity Channel Index (CCI) With Supertrend

CCI indicator oscillates between an overbought and oversold condition and works best in a sideways market.

BUY{
SET C1 = CCI(21,SIMPLE);
SET ST = SUPERTREND(21,2,SIMPLE);
CLOSE > ST AND C1 > 0.01 AND REF(C1,1) < 0.01;}

LONGEXIT{
SET C1 = CCI(21,SIMPLE);
SET ST = SUPERTREND(21,2,SIMPLE);
CLOSE < ST;}

SELL{
SET C1 = CCI(21,SIMPLE);
SET ST = SUPERTREND(21,2,SIMPLE);
CLOSE < ST AND C1 < 0.01 AND REF(C1,1) > 0.01;}

SHORTEXIT{
SET C1 = CCI(21,SIMPLE);
SET ST = SUPERTREND(21,2,SIMPLE);
CLOSE > ST;}

This slideshow requires JavaScript.

Slow & Steady, Algo trading takes up decent share on Dalal Street

origin_cccb28e7b31874b1

Algorithmic trading, a gift of technological advancement to the stock market, is catching up fast with Indian traders and investors.

Markets regulator Sebi recently strengthened the framework for algorithmic trading, making its acceptance more widespread and inconspicuous.

To keep up with changing times, it has become essential for professional traders and arbitrageurs to ramp up speed of execution using contemporary technology tools. And algorithmic trading has come handy.

Algorithmic trading first entered stock markets in mid-1980s, and today it constitutes nearly 70 per cent of total trading volumes in developed markets.

Algorithms are a set of instructions that perform various operations in the market based on the inputs given. Market watchers say the use for algo trading is likely to grow rapidly, as people learn more about financial models, technical indicators and complex, multi-leg option strategies. In many US companies, programmed algos generate technical trades.

In Indian market, many traders use algo signals, which have a set of pre-defined rules, for trading along with their back-tested data base.

“Algorithmic trading can be used regardless of trading strategy. They are used for research and analysis as well as trade execution. One of the early usages of algorithms in stock trading was to help better and faster execution of large orders to reduce their adverse impact on prices, which was the case when such trades used to be executed manually,” says Nitesh Khandelwal, Co-Founder & CEO, QuantInsti.

“It has now become popular with professional traders to enhance profitability of various strategies regardless of market movement, a technique also called alpha of the strategy,” Khandelwal said.

Chandan Taparia, Technical & Derivatives analyst at Motialal Oswal Financial Services, said algo is a platform or a process of using program to follow a defined set of instructions for placing trades to generate profits at a frequency, as such trades are difficult to manage manually. A person can set defined rules based on price, quantity, timing, volumes and any other mathematical model.”

Taparia said the scope for algo trading is huge in India, because of its feasibility, speed and its ability to mitigate human error in execution.

Algorithmic trading can potentially help traders execute orders faster, expand strategy portfolios by using more advanced quantitative tools and remove human emotions that often affect the performance of trading strategies.

“Because of these reasons, algorithmic and quantitative trading strategies are getting more popular, as it can increase the likelihood of success with the backing of the statistical rigor,” Khandelwal said.

Sebi allowed algorithmic trading in India in April 2008 by opening up direct market access to the institutions. Since then, it has grown rapidly across the various asset classes.

“Today, close to 50 per cent of the overall exchange volumes in the F&O segment happen through algorithms. Even in the cash market, the share has grown to more than 30 per cent,” he said.

Algorithmic trading has much higher shares in developed markets, specifically in the US, where more than 70 per cent of overall exchange volumes comes through this route. A part of it can be attributed to the fact that algorithmic trading has existed in the US market for many decades now.

While many people often confuse algorithmic trading with HFT, which is only a specific case or subset of algo trading. One can use algo trading techniques even for fundamental investing with longer investment horizons.

Globally, algo trading is more popular among institutions and professional traders than the individual and retail traders for execution of trades.

The skill and technology needed for algo trading is complex and expensive. Hedge funds, option traders, strategists, pro traders, arbitragers, jobbers, scalper are major users of algo trading.

The key skill needed to succeed in this domain is an understanding of statistics and programming besides, of course, knowledge of the financial markets.

The success rate of algo trade depends on the logic or parameters set in the rule of algo. It’s not a default system, it’s only a platform where people can code their logics as per their understanding and according to a back-tested data base, said Taparia of MOFSL.

In terms of adoption, the Indian market has already crossed the halfway mark of the US and European market levels in last decade. Lower cost of technology, cheaper access to computing power and availability of skilled resources are likely to help fast-track this transition, Khandelwal said.

The future of algo trading adoption will also depend on how the regulations and government policies shape up.

Sebi has been quite prudent with the regulations in this domain. Sebi guidelines on algo trading have also played a positive role in helping its adoption.

From the regulation point of view, the first experiences with the technology have been encouraging. The Indian market has not seen many flash crashes compared with what similar instances in the developed markets.

There are also reports that Sebi might come up with guidelines for use of algo trading by retail traders, which shall help further understanding and acceptance of the domain at a larger scale.

“Algo trading can be beneficial for small-time investors, as it increases liquidity in the market and thereby simplifies the entry and exit process. Increasing depth of algo trading would be good for capital markets as it will remove price inefficiencies in traded securities,” says Ajay Kejriwal, President, Choice Broking.

Source – Economic Times.

EMA with Supertrend As Source

Supertrend is a trend following indicator which can be used to identify upward or downward
trends.

BUY{
SET ST = SUPERTREND(7,3,SIMPLE);
SET MA = EMA(ST,9);
MA > ST AND MA <= (ST * 1.005) AND CLOSE > MA;}

SELL{
SET ST = SUPERTREND(7,3,SIMPLE);
SET MA = EMA(ST,9);
MA < ST AND ST <= (MA * 1.005) AND CLOSE < MA;}

This slideshow requires JavaScript.

Relative Momentum Index (RMI) With WMA.

The Relative Momentum Index (RMI) is a variation of the Relative Strength Index (RSI). While the RMI counts up and down days from today’s close relative to the close n-days ago while the RSI counts days up and down from close to previous close.

BUY{
SET RM = RMI(CLOSE,50,9);
SET MA = WMA(RM,50);
CROSSOVER(RM,MA) AND RM < 20;}

LONGEXIT{
SET RM = RMI(CLOSE,50,9);
SET MA = WMA(RM,50);
CROSSOVER(MA,RM) AND RM > 60;}

SELL{
SET RM = RMI(CLOSE,50,9);
SET MA = WMA(RM,50);
CROSSOVER(MA,RM) AND RM > 60;}

SHORTEXIT{
SET RM = RMI(CLOSE,50,9);
SET MA = WMA(RM,50);
CROSSOVER(RM,MA) AND RM < 20;}

This slideshow requires JavaScript.

Swing Index (SI)

The Swing Index (Wilder) is a popular indicator that shows comparative price strength within a single security by comparing the current open, high, low, and close prices with previous prices.

BUY{
SET TP1 = TP();
SET MA = WMA(TP1,50);
SET SI1 = SI(2);
SET MA1 = EMA(SI1,21);
CROSSOVER(TP1,MA) AND REF(MA1,1) < 0.01 AND REF(CLOSE,1) > REF(OPEN,1);}

LONGEXIT{
SET TP1 = TP();
SET MA = WMA(TP1,50);
SET SI1 = SI(2);
SET MA1 = EMA(SI1,21);
CROSSOVER(MA,TP1) AND REF(MA1,1) > 0.01 AND REF(CLOSE,1) > REF(OPEN,1);}

SELL{
SET TP1 = TP();
SET MA = WMA(TP1,50);
SET SI1 = SI(2);
SET MA1 = EMA(SI1,21);
CROSSOVER(MA,TP1) AND REF(MA1,1) > 0.01 AND REF(CLOSE,1) > REF(OPEN,1);}

SHORTEXIT{
SET TP1 = TP();
SET MA = WMA(TP1,50);
SET SI1 = SI(2);
SET MA1 = EMA(SI1,21);
CROSSOVER(TP1,MA) AND REF(MA1,1) < 0.01 AND REF(CLOSE,1) > REF(OPEN,1);}

This slideshow requires JavaScript.