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;}
BUY{ SET C1 = REF(CLOSE,1); SET C2 = REF(CLOSE,2); SET C3 = REF(CLOSE,3); SET O1 = REF(OPEN,1); SET O2 = REF(OPEN,2); SET O3 = REF(OPEN,3); O3 > C3 AND O2 > C2 AND O1 > C1 AND CLOSE > OPEN AND CLOSE > MAX(HIGH,4);} LONGEXIT{ CLOSE > REF(CLOSE,1) * 1.022;}
The National Stock Exchange of India introduced a facility of managed colocation service, which has been named, ‘Colocation as a Service’ (CaaS).Â
The exchange said the initiative will facilitate small and medium sized members, who otherwise find it difficult to avail colocation facility, due to various reasons including high cost, lack of expertise in maintenance and troubleshooting, etc.
Under this facility, rack in colocation facility will be allotted to empanelled vendors of the exchange along with provision for receiving market data for further dissemination of the same to the trading member of the exchange and the facility to place orders, algorithmic / non-algorithmic, by the trading members from colocation.
BUY{ SET PC3 = REF(CLOSE,3); SET PC2 = REF(CLOSE,2); SET PC1 = REF(CLOSE,1); SET PO3 = REF(OPEN,3); SET PO2 = REF(OPEN,2); SET PO1 = REF(OPEN,1); PC3 < PO3 AND PC2 > PO2 AND PC1 > PO1 AND PC1 > PO3;} SELL{ SET PC3 = REF(CLOSE,3); SET PC2 = REF(CLOSE,2); SET PC1 = REF(CLOSE,1); SET PO3 = REF(OPEN,3); SET PO2 = REF(OPEN,2); SET PO1 = REF(OPEN,1); PC3 > PO3 AND PC2 < PO2 AND PC1 < PO1 AND PC1 < PO3;}
MTF stores price vectors for given period. This function assigns index number specified to vector and stores values as an array. The index number must be unique if the
function is used multiple times in a formula.
BUY : when RSI in 60 min < 25 and RSI in 5 min < 40
SELL : when RSI in 60 min > 90 and RSI in 5 min > 80
BUY{ SET MT = MTF(60,CLOSE,1); SET RSM = RSI(MT,14); SET RS = RSI(CLOSE,14); RSM < 25 AND RS < 40;} LONGEXIT{ SET MT = MTF(60,CLOSE,1); SET RSM = RSI(MT,14); SET RS = RSI(CLOSE,14); RS > 80 AND RSM > 75;} SELL{ SET MT = MTF(60,CLOSE,1); SET RSM = RSI(MT,14); SET RS = RSI(CLOSE,14); RSM > 90 AND RS > 80;} SHORTEXIT{ SET MT = MTF(60,CLOSE,1); SET RSM = RSI(MT,14); SET RS = RSI(CLOSE,14); RSM < 20 AND RS < 40;}
In a bid to relax algorithm trading norms at commodity derivatives exchanges, markets regulator Sebi today raised the limit to process up to 100 orders per second by a user for such trade from the existing limit of 20 orders per second.
The decision has been taken after receiving representations from exchanges along with views of Sebi’s sub committee — Commodity Derivatives Advisory Committee.
“It has been decided to permit exchanges to relax the limit on the number of orders per second from a particular … User-ID up to hundred orders per second,” Securities and Exchange Board of India (Sebi) said in a circular.
The markets regulator asked exchanges to ensure that the limit it provides is subject to its ability to handle the load.
Besides, the regulator has decided to do away with the requirement of empanelment of system auditors by the exchanges for system audit of algorithmic trading.
Algorithmic trading or ‘algo’ in market parlance refers to orders generated at a super-fast speed by use of advanced mathematical models that involve automated execution of trade, and it is mostly used by large institutional investors.
Supertrend indicator is a trend following indicator which can be used to identify upward or downward trends.A stop and reversal (SAR) occurs when the price penetrates a Parabolic SAR level.
BUY :When Current close is greater than supertrend and parabolic sar and current low crossed above ema of period 100 and current open=current low.
SELL: When supertrend crossed above ema of period 13.
BUY{ set su = SUPERTREND(7,3,SIMPLE); set ma1 = EMA(CLOSE,100); set ps = PSAR(0.2,0.02); CLOSE > su and CLOSE > ps and CROSSOVER(LOW,ma1) and OPEN = LOW;} LONGEXIT{ set su = SUPERTREND(7,3,SIMPLE); CROSSOVER(su,EMA(CLOSE,13));}
With several amendments over the years, India provides a good opportunity for Algo traders due to a number of factors such as co-location facilities and sophisticated technology at both the major exchanges; a smart order routing system; and stock exchanges that are well-established and liquid.
Algo trades account for over 43% of India’s stock market turnover. In the US, where retail investors also engage in Algo trades, 90% of the turnover is from automated systems. The global average is 75%.
SEBI was among the first regulators to issue a discussion paper proposing strengthening of rules on Algo trading in August 2016.
With rules in place, Algo trades in India will rise to the global average, market participants said. There are a lot of startups in this space waiting to enter once rules are in place. This will be a big boost for Algo trading.
High Frequency Trading.
Algorithms – step-by-step mathematical procedures – generate automatic trades, conducted by computers, each one racing to be first. And while some computers do receive news about the outside world in electronic format, many high-frequency trading algorithms are simply responding to the hectic world of the electronic trading floor.
Humans still watch the systems, but the computers move far too quickly for us to react to everything they do. To give you a sense of how fast high-frequency trading can be, in the time it takes Usain Bolt to react to the starting pistol, a high-frequency trading platform could complete about 165,000 separate trades.
Now this isn’t quite as insane as it sounds. These computers, all competing with each other, are a lot cheaper and more efficient than human traders trying to match bids to buy and offers to sell. So within reason, automated, high-frequency trading is a good thing. But it’s possible to have too much of a good thing.
High-Frequency Trading are divided into five categories:
First, there are algorithms designed not to lose money while executing a trade that’s been placed by a human. If you try to buy a large block of shares all at once, for instance, you might find that there aren’t enough potential sellers and you’ll have to wait for others to show up.
Other computers may see that you’ve got this large unfilled order and exploit it, perhaps by snapping up shares and selling to you at a profit. To avoid this problem you can ask a computer to slice up your big trade into smaller, more subtle pieces.
Then there are algorithms designed simply to make money by finding buyers and sellers with a little margin between them.
Third, there are algorithms which find statistical relationships between different shares or bonds, and when the statistical relationship fails to hold – even for a moment – they jump in and make a bet that normal service will be resumed. These are called statistical arbitrage algorithms.
So far, so good – it would be hard to find many people in finance who would consider these three types of high-frequency trading to be immoral.
But there are two rather more predatory strategies. One is called algo-sniffing. Here, a super-fast computer tries to find other computers going about their everyday business of buying or selling shares, and figures out what they’re going to do and when.
The algo-sniffer can then get ahead of the game and exploit the slower computer. And of course you could have algo-sniffer-sniffers and algo-sniffer-sniffer-sniffers in a high-frequency arms race. No wonder speed can be so important.
And finally, a particular sub-category of the algo-sniffer is the spoofer, which deliberately makes fake offers designed to lure other computers to show their hands, then cancels the offers. Spoofing might be illegal, or at least against the rules of stock exchanges, but it’s hard to prove that it’s going on.
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