StrategyQuant Algorithmic Platform Review

StrategyQuant for Algorithmic TradersStrategyQuant Algorithmic Platform Review

StrategyQuant is a state-of-the-art platform for building and testing automated trading strategies. Any trader can use the StrategyQuant without the need for programming skills. What makes the platform unique is its ability to evaluate hundreds of strategies at the same time by fully randomizing the market conditions.

StrategyQuant Main Features

The platform can generate algorithmic strategies for trading Fx currencies, Stocks, Indices, ETFs, etc.

StrategyQuant offer two options:

(a) Build your automated strategy from scratch

(b) Find and modify a ready-to-use automated trading strategy

The finalized strategy can be exported and used for various platforms including MetaTrader-4:

(1)  MetaTrader-4

(2)  NinjaTrader

(3)  TradeStation

► StrategyQuant Platform's Website

Algorithmic Trading Features

StrategyQuant offers many features for generating advanced algorithmic strategies:

3d charting(1) Build algorithmic strategies for trading any financial-traded asset in any chart

(2) Customize already-made algorithmic strategies

(3) Use tens of different indicators, including Renko and Range charts

(4) Perform advanced backtesting using Walk-Through analysis and Monte-Carlo Techniques

(5) Optimize your algorithmic strategies by applying the optimal parameters for maximizing performance

StrategyQuant’s Entry/Exit types

□ Profit Target (PT)

□ Exit After X Bars

□ Move Stop Loss to Break-Even

□ Profit Trailing

□ Stop Trailing

□ Exit Rule (Operators + Price + Indicators, ...) 

StrategyQuant -Ensuring the Robustness of Algo Trading Strategies

A curve-fitting strategy jeopardizes the efficiency of the machine learning process. StrategyQuant includes several features for ensuring the robustness of a great number of algorithmic strategies.

Defining Strategy Robustness

Robustness means an algorithmic strategy is able to cope with various market conditions, more specifically a trading strategy should:

(i) work on any data (unknown data)

(ii) work even without parameter re-optimization

(iii) not breaking apart when a number of trades are missing

(iv) not be dependent on any input parameters

StrategyQuant Randomizing Properties

These are some basic properties for randomizing market conditions:

-Randomizing Historical Data (ensuring the algorithmic strategy isn’t too dependent on certain historical data)

-Randomizing Strategy’s Parameters (testing the sensitivity of an algorithmic strategy to changes of some parameter values)

-Skipping Trades Randomly (randomly skipping some trades in order to evaluate the impact on the equity curve)

-Randomizing the Starting Bar

-Randomize the Order of Trades (examining the results of different Drawdowns)

► StrategyQuant Website


StrategyQuant Optimizing Algo Strategies

Every algorithmic trading system incorporates several parameters that decide how the system will behave. These parameters may include comparative constants, the periods of indicators, and others. The process of optimization involves testing the algorithmic system by using various parameter values in order to spot the optimal values of these parameters for any particular asset class or financial-traded asset. The criteria include profitability ratios such as the Return/DD ratio.

StrategyQuant allows the optimization not only of the algorithmic strategy’s parameters but also of other options, trading in specific hours, max numbers of trades to execute on specific days, etc.

Walk-Forward Optimization

Walk-Forward optimization refers to a combination of different optimizations that aim to identify the best parameters that can work on random market conditions.

The value of parameters is optimized based on a past segment of market data, then the performance of the system is tested in forwarding timeframes. This optimization process is repeated over a subsequent number of time segments. Each time segment consists of: (i) optimization part, and (ii) run part.

Walk-Forward Matrix

Walk-Forward Matrix aims to help algorithmic traders identify:

(i) The optimal period for optimizing their strategies

(ii) The best optimization frequency

StrategyQuant allows the optimization not only of the algorithmic strategy’s parameters but also of other options, trading in specific hours, max numbers of trades to execute on specific days, etc.

StrategyQuant -Ranking Strategies Criteria

These are all Strategy’s Quant Ranking criteria, read them carefully as they are all very crucial for creating algorithmic strategies that work:

(1) Net Profit (Total profit/loss of any generated strategy) -IMPORTANT-

(2) Drawdown (measuring the decline from a historical peak)

(3) Max DD % (the maximum (%) drawdown of an algorithmic strategy) -IMPORTANT-

(4) % of Wins (measuring the percentage of winning trades)

(5) Annual % Return (measuring the average annual % return of the algorithmic strategy)

(6) Annual % Return / Max DD % (it is a ratio between annual percentage and maximum percentage drawdown) -IMPORTANT-

(7) Stagnation (Stagnation refers to the maximum number of days during which an algorithmic strategy doesn't make a new high on the balance of equity)

(8) Avg Profit (refers to the average profit in USD for the given period - Daily, Monthly, Yearly)

(9) Return/DD Ratio (measuring the profitability in relation to the maximum drawdown) -IMPORTANT-

(10) Expectancy {it is computed as: (percentage wins * average win) - (percentage losses * average loss) -by Van Tharp}

(11) R-Expectancy {computes the average profit value related to average risk (R) that you can expect from a system over many trades -by Van Tharp}

(12) R-Expectancy Score (R-Expectancy Score adds a score for trades frequency: R-Expectancy * AverageTradesPerYear)

(13) SQN (System Quality Number) (quality metrics developed by Van Tharp)

(14) Growth Stability (measures how stable is the growth of the equity chart of an algorithmic strategy)

(15) Win/Loss Ratio (measuring the ratio of winning trades against the losing trades)

(16) Average Win or Average Loss

(17) Average Bars in Trade (measuring the average number of bars the trade is open)

(18) Degrees of Freedom (relates to the number of criteria that are triggering the price action and determine entry points -the simpler the algorithmic strategy is, the more degrees of freedom it will show)

 

14-DAY FREE TRIAL

StrategyQuant is a commercial software but offers a 14-day free trial which is very useful for all algorithmic traders to find out how the platform really works.

► StrategyQuant 14-day Free Trial

 

StrategyQuant Algorithmic Platform Review

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