Rajandran R Founder of Marketcalls and Co-Founder Algomojo. Full-Time Derivative Trader. Expert in Designing Trading Systems (Amibroker, Ninjatrader, Metatrader, Python, Pinescript). Trading the markets since 2006. Mentoring Traders on Trading System Designing, Market Profile, Orderflow and Trade Automation.

K-Lintra – Hourly Positional Trading System for Nifty Futures

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What is K-Lintra?

K-Lintra is a trend following system which uses the combination of Kaufman adaptive moving average and Linear regression-based volatility channel. It uses an adaptive approach to switch the time period and thereby dynamically able to adapt to changing market dynamics.

Primary objective of the k-lintra trend following system is to reduce the number of trades and thereby making the system completely independent of slippages and thereby bringing consistency in return and maximizing the overall gain of the portfolio.

What is a Trend Following System?

According to Wikipedia, Trend following or trend trading is a trading strategy according to which one should buy an asset when its price trend goes up, and sell when its trend goes down, expecting price movements to continue.

Traders who employ a trend following strategy do not aim to forecast or predict specific price levels; they simply jump on the trend (when they perceived that a trend has established with their own peculiar reasons or rules) and ride it.

How to classify the market based on market volatility?

K-Lintra uses a PercentRank Based Smooth ATR to Predict Change in Volatility, in-order to identify market regime shift from low volatility zone to high volatility zone and vice versa.

Basically Volatile seasons are classified into four categories

1)Low volatile season
2)Extremely low volatile season
3)High volatile season
4)Extremely high volatile season

input parameters are dynamically changed based on the changing volatile market dynamics. Volatility is measured on the hourly timeframe.

Strategy is ready to plug and play with Automated trading tools. However one can play with manual limit order execution as well.

Backtesting Performance

Inorder to test the system parameters following parameters are used

ParametersValue
Trading InstrumentNifty Futures
Backtesting TimeframeHourly (Continous Data)
Backtest LengthJan 2011 – May 2020
Strategy TypeVolatility based Trend Following
Position Size1 Lot (Fixed Position Sizing)
Slippage + Commissions0.03%
Trading Capital RS 3,00,000
Trading Leveragemax of 4 times

Backtesting Statistics

Key Backtesting Performance Metrics

Key Performance MetricsValue
Sharp Ratio1.14
Max System Drawdown 1.58L/lot (unhedged risk)
Calmar Ratio (CAR/MDD)1.02
Recovery Factor7.73
Profit Factor1.71
Payoff Ratio1.86
Risk-Reward Ratio2.06

Trading System Parameters

ParametersValues
ATR Value100
Length 1 – Extreme Low Volatility90
Length 2 – Low Volatility50
Length 3 – Extreme High Volatility90
Length 4 – High Volatility200
Is the strategy looks into futureNo
Does the Strategy RepaintsNo
Does the Strategy OptimizedYes
Is it over-optimized or Curvefitted?No
Does it Pass the System Validation Test?Yes

kLinTRA V5 – Equity Curve

K-Lintra – Drawdown Curve

K-Lintra Absolute Profit Table in Multiples of Thousands

Points Made – Year wise

YearPoints Made per lot (Net of Slippages/Commissions)
20111299
20121510
20131064
20141621
20152144
2016831
2017509
20181805
20193200
20202079 (upto May 2020)

Rajandran R Founder of Marketcalls and Co-Founder Algomojo. Full-Time Derivative Trader. Expert in Designing Trading Systems (Amibroker, Ninjatrader, Metatrader, Python, Pinescript). Trading the markets since 2006. Mentoring Traders on Trading System Designing, Market Profile, Orderflow and Trade Automation.

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