StrategyQuant is a sophisticated platform that allows traders without programming skills to create, optimize, and backtest advanced automated trading strategies from scratch. The next logical step towards the evolution of automated trading.
StrategyQuant Review: The Evolution of Strategy Building
StrategyQuant users can create or find already-made automated strategies to trade any financial market (Forex, Equities, Metals, Soft Commodities, etc.). The only thing a trader must do is to select a market and a timeframe. Afterward, StrategyQuant starts to generate automated strategies. The trader can now choose the strategies that showed the best performance. In addition, he can test, and optimize them towards randomness.
□ StrategyQuant operates in four (4) modes: building, re-testing, improvement, and optimization
□ Building (from scratch) and optimizing automated trading systems for every financial market
□ Generating and testing thousands of random automated strategies (within hours)
□ Applying automated data-mining algorithms to generate EAs for MetaTrader4, TradeStation, and NinjaTrader platforms
□ 14-day trial (The StrategyQuant trial version fully functional and it is not limited compared to the full package. The trial includes also data from EUR/USD, USD/CHF, GBP/USD, and USD/JPY).
StrategyQuant Review: Additional Features
These are some key features of the platform:
-Find easily thousands of automated strategies trading currencies, stocks, derivatives, etc.
-Create EAs for MetaTrader 4, TradeStation, NinjaTrader, and MultiCharts without programming skills
-Walk-Forward Optimization, Walk-Forward Matrix (3d Charts), and Monte Carlo methods
-Random strategy generation (avoid curve fitting)
-Advanced back-testing and optimization modules (External tick-data support)
-Use a wide variety of standard and external indicators
-Strong customer support and an active community of thousands of members
StrategyQuant Review: Key Functions
These are some interesting functions of StrategyQuant:
The walk-forward optimizer allows spotting the results of an automated strategy when it is optimized regularly. The walk-forward optimizer may identify the ideal timeframe to optimize the key parameters
-It offers 40 indicators
-It offers candlestick and other patterns
-Four (4) entry orders (Enter at Market, Stop, Limit, Reverse)
-Seven (7) output orders
-Trailing stops and an intelligent stop loss
-Filtering automated-strategies into timeframes
In general, Monte Carlo methods use randomness (repeated random sampling) to solve problems that might be deterministic in principle. The StrategyQuant Monte Carlo testing allows the evaluation of the quality of a trading strategy especially as concerns the potential for long-term profitability when trading real capital.
Using wide options for filtering automated strategies makes it easier to determine the results (profit factor, how much profit it will probably earn, maximum risk is, etc.)