Introduction to Quant Strategy Inventor

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Introduction to Quant Strategy Inventor

Flexible Quantitative Strategy Builder for Forex and Stock Market

1. Quant Strategy Inventor – Flexible Quantitative Strategy Builder for Forex, Stock and Future Market
Quant Strategy Inventor is the powerful Excel based software to create profitable strategy for Stock, Forex and Future market. With Quant Strategy Inventor, you can create the profitable trading strategies. You can analyse and predict financial markets. You can construct your own active and passive portfolio like the fund manager. You can do all this without pain of learning any programming language like C++, C sharp, MQL4, MQL5, Easy Language, etc. Instead, you can do everything in your Excel Spreadsheet from backtesting to optimization of your trading strategy using simple spreadsheet formula. Quant Strategy Inventor is so flexible so you can even build your strategy using VBA code if you prefer that way. To guide you the way through our Quant Strategy Inventor, we ship our Quant Strategy Inventor with several example-trading strategies both created in spreadsheet and VBA. So you can get going with your strategy building immediately.

2. What is Quant Strategy Inventor?
Quant Strategy Inventor is the spreadsheet-oriented software featuring powerful backtesting simulation, optimization, worst-case scenario analysis, technical analysis and economic analysis for your trading and investment. Quant Strategy Inventor is not statistical software like SAS or SPSS and it is strongly oriented through trading and investment application. The Quant Strategy Inventor was created as an Excel Add-in meaning that it runs in your Excel Spreadsheet. With user friendly and highly customizable feature of Excel, you can generate profitable strategy in less time with less labour cost. Advantage of using Quant Strategy Inventor is that you do not have to learn any third party programming language to develop and test your trading ideas. You can simply create your sophisticated trading strategies using Excel’s built in spreadsheet formula.  The good news is that you do not have to spend hours of time to learn new programming language like C++ or C Sharp or MQL4 or easy language at all. Without knowing any programming language, you can still build powerful trading strategy with our Quant Strategy Inventor. This is not necessarily meant that people with coding ability can’t use our Quant Strategy Inventor. If you wish, you can create your trading logic in full VBA language too. You can build as complex model as you can. You can still use our backtesting algorithm to test your trading ideas created from your VBA code. Even though our Quant Strategy Inventor is the spreadsheet-oriented software, we still use the rigorous power of C++ and C sharp for many of its calculation algorithm.
You can visualize your forex and stock market data with plenty of flexible charting techniques already built in Excel. Additionally we offer over 80 technical indicators for your trading model construction. In your trading model, you can include many real world economic variables like country’s GDP or unemployment rate or commodity prices. You can load any free data or commercial data to build your own trading strategies. Test if the trading idea is valid with our Quant Strategy Inventor. Check its worst case- scenario with our spread and slippage simulator. You can even build optimal portfolio for your serious capital using our Quant Strategy Inventor.

Figure 1: Menu to access Quant Strategy Inventor Modules. We have created some sample spreadsheet with some simple trading strategy.

Figure 2: Backtesting and optimization results on sample Strategy.

3. Benefit of Quant Strategy Inventor
The biggest benefits of Quant Strategy Inventor is that firstly you can build profitable trading strategies in your spreadsheet without learning any third party languages like C++, C sharp, MQL4, MQL5, easy language, MatLab syntax, etc. Remember that Excel is mostly used tools in any business and finance world. Most of traders in London and Wall Street are capable of using Excel for their trading and investment analysis. Even if they use some third party platform, they never give up using Excel because simply it is so flexible and easy to use.
Secondly, you can add any economic or real world variables in your trading strategies with our Quant Strategy Inventor. For example, if US retails sales data are important for your trading, then just grab the data from net and paste them right next to your spreadsheet trading strategies to test what kind of impact they have on your strategy. Once you have built trading strategies in your spreadsheet, you can do various things like backtesting, optimization, and portfolio construction with our Quant Strategy Inventor or even more is possible. Below is the breakdown of benefits of our Quant Strategy Inventor.
Bulk data downloading facility for US and Non-US stock market data. Visualize forex and stock data with over 80 technical analysis in your Spreadsheet. For Forex data simply use Meta Trader to pull some free data for analysis and build trading strategy.
Quickly test various trading ideas in your spreadsheet without writing code. You can literally create any trading strategies you can imagine with our Quant Strategy Inventor.
Test multiple trading strategies at the same time. Currently you can test up to 100 strategies at the same time.
Including economic variables are tough in many trading platforms. In our Quant Strategy Inventor, you can include any commercial or non-commercial economic data into your trading strategy without incurring any additional development cost.
You can apply your trading strategy to many different instruments across different market. For example, you can build trading strategy for forex.  If you are curious about the profitability of your strategy in stock market, then you can quickly copy and paste stock market data in your trading strategy template to test their profitability. It is only matter of few clicks to know the truth in your spreadsheet.
Build trading strategy at the same time as you manage your portfolio.
If you prefer to hide your trading strategy, you can still build your trading strategy using full VBA code. Therefore, you have an additional option to use VBA code for your strategy building if you wish.
Quant Strategy Inventor include many other statistical algorithm to support your trading and investment. For example, you can use our Augmented-Dickey Fuller test to build pair trading strategy. You can use our Mean Variance portfolio algorithm to manage many different trading strategies. You have an access to nine different forecasting algorithm and ARIMA forecasting model to gauge overall market direction. You can also check seasonal behaviour of any stock market data using our Quant Strategy Inventor. Besides these algorithms, there are many other algorithms to support your trading and investment with our Quant Strategy Inventor.
You can simulate trading cost with our Quant Strategy Inventor to estimate your trading model in more realistic sense. You can apply slippage and widened spread for forex. You can also simulate different level of fees for stock trading in one button click.

Figure 3: Candlestick Chart of EURUSD ticker with simple moving average 13, simple moving average 40, Bollinger bands and Parabolic SAR and T3 moving average.

Figure 4: Mean Variance Portfolio construction with the portfolio of the popular US stock

Figure 5: S&P 500 index vs Stock data. This analysis was performed through Market Analysis page in our Quant Strategy Inventor.

4. Main Feature of Quant Strategy Inventor
Quant Strategy Inventor consists of four modules including Basic Module 1, Analytic Module 1, Analytic Module 2 and Help Module. Each module is made of multiple pages of Graphical User Interface where you can execute your command through. Below we list the functionality of each module.

1) Basic Module 1
Data Download: You can download any stock market data from Yahoo finance. This include both US and non-US stocks. You can even download bulk symbols in just one click. You can also control and select various download options through our graphical user interface.
Simulate financial market data: You can simulate various financial market data with 11 different equations. Simulated data can be often important when you want to test your trading ideas. You can view the equations from the Appendix page of this document.

Figure 7: Graphical User Interface for Technical analysis. You can load up to three instruments at the same time. You can do various technical analysis and you can study the co movement of two or three instruments to construct pair trading strategy.
2) Analytic Module 1
Technical Analysis: You can access to 22 technical indicators from Graphical User Interface. You can access to over 80 different technical indicators through your spreadsheet formula. With Graphical User Interface, you can automatically generate candlestick chart together with various technical indicators. You can even line up three different instruments in the same spreadsheet. This is particularly useful when you want to construct pair trading strategy or beta driven portfolio. You can also save your favourite technical indicators in your spreadsheet. Later you can load them in one button click.
Backtesting and optimization for Forex: You can backtest and optimize your trading ideas for Forex market data. You can build your trading logics with technical indicators or economic data or mathematical models or price action. Or you can combine all of them together in your model at the same time. After you have constructed your trading model in your spreadsheet or VBA, you can test and refine your trading strategy using our backtesting and optimization simulator. You can also perform worst-case scenario analysis for your trading strategy with our spread and slippage simulator.
Backtesting Stock: You can use the above the same features for stock market data too.

Figure 8: Graphical User Interface for Backtesting Forex data. You can backtesting, optimization and spread – slippage simulation.

3) Analytic Module 2
Exploratory Analysis:  In Exploratory Analysis, you can generate descriptive statistics and histogram for your data. You can also generate ACF and PACF plots at the same time. All this analysis can be done in one button click.
Stationary Analysis: In stationary analysis, you can test stationarity of your time series using Augmented Dickey Fuller test. This is very important assumption for your trading model creation. It is also an important testing tool to create some advanced trading strategy like statistical arbitrage.
Seasonality Analysis: With seasonality analysis, you can check some regular cyclic movement in your stock data. You can visualize those periodic movements in your spreadsheet and in chart. When you have found some seasonality in your data, you can build your trading strategy accordingly to capture such a dynamics.
Forecasting Algorithm: With Forecasting algorithm, you have access to nine exponential smoothing forecasting model. You can create popular damped trend exponential smoothing model or damped trend with additive seasonal model. You can also transform your data with log or box-cox automatically prior to building forecasting model. The routines are fully automatic and nine exponential smoothing models are listed in the appendix in this document.
ARIMA: Autoregressive Integrated Moving average techniques are commonly used forecasting methods for economist.  With our Quant Strategy Inventor, you can access to both ARIMA and SARIMA forecasting model.
Portfolio Analysis: With our Quant Strategy inventor, you can construct popular Mean-Variance portfolio model with one just one-button click. If you prefer to trade with diversification in your portfolio. This is great tool for you. This is also quite handy tools to construct passive portfolio model when you have serious capital to trade.
Market Analysis: With Market Analysis, you can calculate market alpha, beta, CAPM return as well as other useful investment statistics. You can use this feature when you want to analyse your stock or forex data against market index like S&P500, FTSE100, etc.

Figure 9: Graphical User Interface for forecasting page. You can use nine different exponential smoothing forecasting model with/without Log and Box-Cox transformation.

4) Help module
Help module does not include any algorithms or calculation. The most important function of the Help module is to get your installation done and license activated. We will explain about this in the separate installation page.

5. Strategy Creation with Backtesting, optimization and Cost Simulation

The focus of Quant Strategy Inventor is to build profitable trading and investment strategy.  The biggest advantage of our Quant Strategy Inventor comes from its flexibility and comprehensiveness exposing its interface through Excel Spreadsheet. Instead of writing C++ or C sharp code running at background, you can develop transparent and easy to understand algorithm for your strategy. At the same time, you can build the trading strategy using full VBA code too if you wish and if you believe that is more efficient for your strategy building. Even if you build your strategy in full VBA code, you can still access all the features available from our Quant Strategy Inventor. Currently you can use our Quant Strategy Inventor to build strategy for Forex, Stock, Future and Commodities Market. You can include any technical analysis, economic variables, price action and pattern recognition techniques in your trading strategy. Once you have created some brilliant trading strategy, you can do fine-tuning of your trading strategy with optimizer built in Quant Strategy Inventor. Furthermore, you can do cost simulation for your trading strategy for more realistic estimation of your trading outcome. After the simulation, Quant Strategy Inventor will report trading statistics for both buy and sell positions as well as combined statistics.

Figure 10: Graphical User Interface for Stock Ticker Backtesting

Figure 11: Sample CCI cross over trading strategy utilizing take profit and stop loss together. To do fine tuning the parameters of the CCI strategy, it require the optimization with 7040 backtesting.

Figure 12: After optimization, you can sort the backtesting results according to your preferred trading statistics.

6. Sample Screenshots from Quant Strategy Inventor

Figure 13: Two ticker loaded together to find out potential pair trading opportunity.

Figure 14: Exponential Smoothing forecasting for FTSE250, S&P500 and DAX indices.

Figure 15: Histogram, Autocorrelation Function and Partial Autocorrelation Function for EURUSD.

Figure 16: ARIMA (1, 1, 1) Forecasting for EURUSD Daily Data.

7. Appendix – Math and Theory behind Quant Strategy Inventor

1) Technical indicators accessible from Graphical User Interface
Currently you can access to these indicators through graphical user interface. We will support more indicators from our Graphical user Interface in the future.
Simple Moving Average
Linear Weighted Moving Average
Exponential Moving Average
Double Exponential Moving Average
Parabolic SAR
Bollinger Bands
Triple Exponential Moving Average
Kaufman Adaptive Moving Average
Triple Exponential Moving Average (T3)
Commodity Channel Index
Standard Deviation
Relative Strength Index
Stochastic
Average Directional Movement Index
Moving Average Convergence/Divergence
Momentum
Williams’ %R
Rate of Change
Money Flow Index
TRIX (1-day Rate-Of-Change (ROC) of a Triple Smooth EMA)
Ultimate Oscillator
Average True Range
On Balance Volume
Chaikin A/D Line

2) Technical indicators accessible from your Excel Spreadsheet
You can call these indicators using worksheet formula.
Chaikin A/D Line
Chaikin A/D Oscillator
On Balance Volume
Hilbert Transform – Dominant Cycle Period
Hilbert Transform – Dominant Cycle Phase
Hilbert Transform – Phasor Components
Hilbert Transform – SineWave
Hilbert Transform – Trend vs Cycle Mode
Average Price
Median Price
Typical Price
Weighted Close Price
Average True Range
Normalized Average True Range
True Range
Two Crows
Three Black Crows
Three Inside Up/Down
Three-Line Strike
Three Outside Up/Down
Three Stars In The South
Three Advancing White Soldiers
Abandoned Baby
Advance Block
Belt-hold
Breakaway
Closing Marubozu
Concealing Baby Swallow
Counterattack
Dark Cloud Cover
Doji
Doji Star
Dragonfly Doji
Engulfing Pattern
Evening Doji Star
Evening Star
Up/Down-gap side-by-side white lines
Gravestone Doji
Hammer
Hanging Man
Harami Pattern
Harami Cross Pattern
High-Wave Candle
Hikkake Pattern
Modified Hikkake Pattern
Homing Pigeon
Identical Three Crows
In-Neck Pattern
Inverted Hammer
Kicking
Kicking – bull/bear determined by the longer marubozu
Ladder Bottom
Long Legged Doji
Long Line Candle
Marubozu
Matching Low
Mat Hold
Morning Doji Star
Morning Star
On-Neck Pattern
Piercing Pattern
Rickshaw Man
Rising/Falling Three Methods
Separating Lines
Shooting Star
Short Line Candle
Spinning Top
Stalled Pattern
Stick Sandwich
Takuri (Dragonfly Doji with very long lower shadow)
Tasuki Gap
Thrusting Pattern
Tristar Pattern
Unique 3 River
Upside Gap Two Crows
Upside/Downside Gap Three Methods
Any many more

3) Simulation Model to generate time series data
The data generating process of the three linear patterns include:

 Autoregressive 1 (AR 1)
y_t=0.6y_(t-1)+ε_t , a representative linear model.

 Autoregressive 2 (AR 2)
y_t=0.4y_(t-1)-0.3y_(t-2)+ε_t , a stationary AR model.

 Moving Average 2 (MA 2)
y_t=ε_t-0.4ε_(t-1)-0.3ε_(t-2)  .
The data generating process of the five nonlinear patterns include:
Threshold autoregressive (TAR)
y_t=0.9y_(t-1)+ε_t   for  |y_(t-1) |≤1  ,
y_t=-0.3y_(t-1)+ε_t   for  |y_(t-1) |>1  , an example considered by Tong (1983).

 Nonlinear moving average (NMA)
y_t=ε_t-0.3ε_(t-1)+0.2ε_(t-2)+0.4ε_(t-1) ε_(t-2)-0.25ε_(t-2)^2  , where the final two terms give a nonlinear MA model that is typically noninvertible.

 Bilinear (BL)
y_t=0.7y_(t-1) ε_(t-2)+ε_t , a bilinear model having the same covariance properties as a white noise (Granger & Andersen, 1978).

 Bilinear AR (BL AR)
y_t=0.4y_(t-1)-0.3y_(t-2)+0.5y_(t-1) ε_(t-1)+ε_t  , a model containing both linear and bilinear terms.

 Bilinear ARMA (BL ARMA)
y_t=0.4y_(t-1)-0.3y_(t-2)+0.5y_(t-1) ε_(t-1)+0.8ε_(t-1)+ε_t  .

4) Exponential Smoothing Equations used in Forecasting Algorithm
A. Notations
Ft = Forecast at period t
At = Actual at period t
et = errors at period t
Lt = Level at period t
Tt = Trend at period t
St = Seasonality at period t
α = smoothing parameter alpha for level
β = smoothing parameter beta for trend
γ = smoothing parameter gamma for seasonality
Ф = damping factor phi for trend
m = subscript “m” indicates the seasonality of the time series

∑_(i=1)^h▒〖ϕ^i T_t=(ϕ+ϕ^2+ϕ^3+ ∙∙∙∙∙∙ +ϕ^h)T_t 〗

B. Single Exponential Smoothing (SES)-ETS (A, N, N)
Non Error Correction Version
L_t=αA_t+(1-α) L_(t-1)
F_(t+h)=L_t

Error Correction version
L_t=L_(t-1)+αe_t
F_(t+h)=L_t

C. Additive Seasonality Exponential Smoothing (ASES) -ETS (A, N, A)
Non Error Correction Version
L_t=α〖(A〗_t-S_(t-m))+(1-α) L_(t-1)
S_t=γ(A_t-L_(t-1))+(1-γ) S_(t-m)
F_(t+h)=L_t+S_(t-h+m)

Error Correction version
L_t=L_(t-1)+αe_t
S_t=S_(t-m)+γe_t
F_(t+h)=L_t+S_(t-h+m)

D. Multiplicative Seasonality Exponential Smoothing (MSES) -ETS (A, N, M)
Non Error Correction Version
L_t=α A_t/S_(t-m) +(1-α)(L_(t-1))
S_t=γ(A_t/L_(t-1) )+(1-γ)S_(t-m)
F_(t+h)=L_t  S_(t-m+h)

Error Correction version
L_t=L_(t-1)+α(e_t/S_(t-m) )
S_t=S_(t-m)+γ(e_t/L_(t-1) )
F_(t+h)=L_t  S_(t-m+h)

E. Linear Trend Exponential Smoothing (LTES)-ETS (A, A, N)
Non Error Correction Version
L_t=αA_t+(1-α)(L_(t-1)+T_(t-1))
T_t=β(L_t-L_(t-1))+(1-β) T_(t-1)
F_(t+h)=L_t+〖hT〗_t

Error Correction version
L_t=L_(t-1)+T_(t-1)+αe_t
T_t=T_(t-1)+αβe_t
F_(t+h)=L_t+〖hT〗_t

F. Damped Trend Exponential Smoothing (DTES)-ETS (A, Ad, N)
Non Error Correction Version
L_t=αA_t+(1-α)(L_(t-1)+ϕT_(t-1))
T_t=β(L_t-L_(t-1))+(1-β) 〖ϕT〗_(t-1)
F_(t+h)=L_t+∑_(i=1)^h▒〖ϕ^i T_t 〗

Error Correction version
L_t=L_(t-1)+〖ϕT〗_(t-1)+αe_t
T_t=〖ϕT〗_(t-1)+αβe_t
F_(t+h)=L_t+∑_(i=1)^h▒〖ϕ^i T_t 〗

G. Linear Trend Additive Seasonality Exponential Smoothing (LTASES)-ETS (A, A, A)
Non Error Correction Version
L_t=α〖(A〗_(t-) S_(t-m))+(1-α)(L_(t-1)+T_(t-1))
T_t=β(L_t-L_(t-1))+(1-β) T_(t-1)
S_t=γ(A_t-L_(t-1)-T_(t-1))+(1-γ) S_(t-m)
F_(t+h)=L_t+〖hT〗_t+S_(t-m+h)

Error Correction version
L_t=L_(t-1)+T_(t-1)+αe_t
T_t=T_(t-1)+αβe_t
S_t=S_(t-m)+γe_t
F_(t+h)=L_t+〖hT〗_t+S_(t-m+h)

H. Linear Trend Multiplicative Seasonality Exponential Smoothing (LTMSES)-ETS (A, A, M)
Non Error Correction Version
L_t=α A_t/S_(t-m) +(1-α)(L_(t-1)+T_(t-1))
T_t=β(L_t-L_(t-1))+(1-β) T_(t-1)
S_t=γ(A_t/(L_(t-1)+T_(t-1) ))+(1-γ)S_(t-m)
F_(t+h)=〖(L〗_t+〖hT〗_t)S_(t-m+h)

Error Correction version
L_t=L_(t-1)+T_(t-1)+α(e_t/S_(t-m) )
T_t=T_(t-1)+αβ(e_t/S_(t-m) )
S_t=S_(t-m)+γ(e_t/(L_(t-1)+T_(t-1) ))
F_(t+h)=〖(L〗_t+〖hT〗_t)S_(t-m+h)

I.  Damped Trend Additive Seasonality Exponential Smoothing (DTASES)-ETS (A, Ad, A)
Non Error Correction Version
L_t=α〖(A〗_t-S_(t-m))+(1-α)(L_(t-1)+〖ϕT〗_(t-1))
T_t=β(L_t-L_(t-1))+(1-β) 〖ϕT〗_(t-1)
S_t=γ(A_t-L_(t-1)-〖ϕT〗_(t-1))+(1-γ) S_(t-m)
F_(t+h)=L_t+∑_(i=1)^h▒〖ϕ^i T_t 〗+S_(t-m+h)

Error Correction version
L_t=L_(t-1)+ϕT_(t-1)+αe_t
T_t=〖ϕT〗_(t-1)+αβe_t
S_t=S_(t-m)+γe_t
F_(t+h)=L_t+∑_(i=1)^h▒〖ϕ^i T_t 〗+S_(t-m+h)

J.  Damped Trend Multiplicative Seasonality Exponential Smoothing (DTMSES)–ETS (A, Ad, M)
Non Error Correction Version
L_t=α A_t/S_(t-m) +(1-α)(L_(t-1)+〖ϕT〗_(t-1))
T_t=β(L_t-L_(t-1))+(1-β) 〖ϕT〗_(t-1)
S_t=γ(A_t/(L_(t-1)+〖ϕT〗_(t-1) ))+(1-γ)S_(t-m)
F_(t+h)=〖(L〗_t+∑_(i=1)^h▒〖ϕ^i T_t)〗 S_(t-m+h)

Error Correction version
L_t=L_(t-1)+〖ϕT〗_(t-1)+α(e_t/S_(t-m) )
T_t=〖ϕT〗_(t-1)+αβ(e_t/S_(t-m) )
S_t=S_(t-m)+γ(e_t/(L_(t-1)+〖ϕT〗_(t-1) ))
F_(t+h)=〖(L〗_t+∑_(i=1)^h▒〖ϕ^i T_t)〗 S_(t-m+h)

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