Heikinashi Cntd

BUY : when TMA(50 Period) < Close and current volume > previoue volume*20
SELL : when TMA (50 Period)> Close and current volume > previoue volume*20

[HEIKINASHI]
BUY{
SET tma1 = TMA(CLOSE,50);
CLOSE > tma1 and VOLUME > REF(VOLUME,1)*20;}

LONGEXIT{
CLOSE < OPEN;}

SELL{
SET tma1 = TMA(CLOSE,50);
tma1 > CLOSE and VOLUME > REF(VOLUME,1)*20;}

SHORTEXIT{
CLOSE > OPEN;}

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Use Of MTF( Multi Time Frame )

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;}

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Algorithmic Trading share in total turnover grows to 50% in 8 years

Algorthmic-Trading-700x372

 

Algorithmic trading in India across the cash and derivatives market as a percentage of total turnover has increased up to 49.8% in eight years from merely 9.26% (average) in 2010. In March this year, 44.8% of the cash market volume and 48.2% of the equity derivatives market was driven by algo, showed NSE data. On the BSE, 37.22% of trade in March 2018 was driven by algo trading.

The daily turnover of equity market is around Rs 25,000 crore to Rs 30,000 crore, and in the F&O market, it is around Rs 3.5 lakh crore to Rs 4 lakh crore on a daily basis. However, as far as awareness of the retail investor is concerned, it is less in India. This is mainly because it requires specialised skills in addition to tools.

Sebi’s recent announcement on steps for strengthening algo trading through shared co-location has boosted the sentiments of algo solutions providers.

Experts believes that in the near future human-machine interaction could go to the next level. Through Deep Learning, AI, algorithms will self-correct and adapt to dynamic markets. Algos will be everywhere, in HFT, mid-to-low frequency, arbitrage, scalping, hedging, market making and anything you can define to a machine.

The efficiency of almost any trading done on the exchanges can be improved by leveraging technology. Automation of the trading process not just improves the efficiency of the trading participants, but also improves the efficiency of the market itself — arbitrageurs use automation to rectify pricing anomalies; and market makers use the power of technology to improve liquidity by providing continuous buy and sell quotes which automatically adjust to events and risks in the market.

15 Mins High/ Low Break – Most Popular Strategy

BUY{
SET HV = LOOKUP(TODAY,091500,HIGH);

LONGEXIT{
SET HV = LOOKUP(TODAY,091500,HIGH);
SET LV = LOOKUP(TODAY,091500,LOW);
SET TRG = HV * 1.01;
LOW < LV AND REF(CLOSE,1) > LV OR HIGH > TRG;}

SELL{
SET LV = LOOKUP(TODAY,091500,LOW);

SHORTEXIT{
SET HV = LOOKUP(TODAY,091500,HIGH);
SET LV = LOOKUP(TODAY,091500,LOW);
SET TRG = LV * 0.99;
HIGH > HV AND REF(CLOSE,1) < HV OR TRG > LOW;}

Sebi eases Algo trade rules in commodity exchanges

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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.

 

 

 

 

HEIKIN-ASHI

Heikin-Ashi Candlesticks use the open-close data from the prior period and the open-high-low-close data from the current period to create a combo candlestick.

[HEIKINASHI]
BUY{
SET hv = MAX(CLOSE,10);
CLOSE > hv and REF(CLOSE,2) < REF(OPEN,2) and REF(CLOSE,1) > REF(OPEN,1) and CLOSE > OPEN and CLOSE > REF(CLOSE,1);}

LONGEXIT{
REF(CLOSE,1) < REF(OPEN,1);}

SELL{
SET Lv = MIN(CLOSE,10);
CLOSE < lv and REF(CLOSE,2) > REF(OPEN,2) and REF(CLOSE,1) < REF(OPEN,1) and CLOSE < OPEN and CLOSE < REF(CLOSE,1);}

SHORTEXIT{
REF(CLOSE,1) > REF(OPEN,1);}

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Moving Average Convergence / Divergence (MACD) AND Commodity Channel Index (CCI)

CCI indicator oscillates between an overbought and oversold condition and works best in a sideways market. The MACD fluctuates above and below the zero line as the moving averages converge, cross and diverge. Traders can look for signal line crossovers, centerline crossovers and divergences to generate signals.

BUY{
SET macd1 = MACD(100,21,9,SIMPLE);
SET macdsig = MACDSignal(100,21,9,SIMPLE); 
SET cci1 = CCI(50,SIMPLE); 
CROSSOVER(macdsig,macd1) and cci1 > 0.1;}

LONGEXIT{
SET macd1 = MACD(100,21,9,SIMPLE);
SET macdsig = MACDSignal(100,21,9,SIMPLE); 
SET cci1 = CCI(50,SIMPLE); 
CROSSOVER(macd1,macdsig) and cci1 < 0.1;}

SELL{
SET macd1 = MACD(100,21,9,SIMPLE);
SET macdsig = MACDSignal(100,21,9,SIMPLE);
SET cci1 = CCI(50,SIMPLE); 
CROSSOVER(macd1,macdsig) and cci1 < 0.1;}

SHORTEXIT{
SET macd1 = MACD(100,21,9,SIMPLE);
SET macdsig = MACDSignal(100,21,9,SIMPLE); 
SET cci1 = CCI(50,SIMPLE); 
CROSSOVER(macdsig,macd1) and cci1 > 0.1;}

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Weighted Moving Average (WMA) With Bollinger Bands

A Weighted Moving Average places more weight on recent values and less weight on older values. Bollinger bands rely on standard deviations in order to adjust to changing market conditions. When a stock becomes volatile the bands widens (move further away from the average). Conversely, when market becomes less volatile the bands contracts (move closer to the average).

SMA(CLOSE,9)  : Simple Moving Average of 9 period on Close
EMA(ma1,9)      : Exponential Moving Average of 9 period on SMA
WMA(ma2,5)    : Weighted Moving Average of 5 period on EMA

BUY{
SET ma1 = SMA(CLOSE,9);
SET ma2 = EMA(ma1,9);
SET ma3 = WMA(ma2,5);
SET vol = SMA(VOLUME,10);
SET bbt = BBT(CLOSE,10,2,SIMPLE); 
SET smab = SMA(bbt,50);
CROSSOVER(ma1,ma3) and CLOSE > bbt and VOLUME > vol and CLOSE > smab AND EMA(ma1,9) > SMA(bbt,50);}
SELL{
SET ma1 = SMA(CLOSE,9);
SET ma2 = EMA(ma1,9);
SET ma3 = WMA(ma2,5);
CROSSOVER(ma3,ma1);}

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Standard Deviation-SuperTrend Strategy

Standard Deviation is a common statistical calculation that measures volatility. Supertrend is a trend following indicator which can be used to identify upward or downward trends.

BUY : when current SD is greater than maximum EMA on SD for previous 40 period.
SELL: when Supertrend crossed above Current close.

BUY{
SET sd = SDV(CLOSE,21,2,SIMPLE); 
SET st = SUPERTREND(200,5,SIMPLE); 
SET av = EMA(sd,50);
SET hv = MAX(av,40);
sd > hv and CLOSE > st;}
SELL{
set sd = SDV(CLOSE,21,2,SIMPLE); 
set st = SUPERTREND(200,5,SIMPLE); 
CROSSOVER(st,CLOSE);}

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