(+) Algorithmic Trading

 Quantamental Trading Approach

Quantamental is a new trading approach that combines quantitative and fundamental analyses in order to forecast future market conditions. Trading and data mining will share a common ground in the near future.

In general, forecasting is the process of predicting future market conditions based on past and present market conditions, and on the analysis of key trends. All forecasting methods are divided into two broader categories:

(i) Quantitative methods, based on mathematical models, and

(ii) Qualitative methods, based on educated guessing.

(1) Quantitative Forecasting

Quantitative forecasting means forecasting future data as a function of past data. Quantitative forecasting requires the existence of past numerical data and assumes that some of the recognizable patterns in the data series are expected to continue into the future. Quantitative forecasting is more effective in short and mid-term-range decision-making.

Two (2) Quantitative Methods

There are two main types of quantitative methods:

(i) time-series methods (using simple historical trends and patterns in the data series to generate forecast)

(ii) explanatory methods (using additional data as inputs into the forecasting model and attempting to combine two or more variables)

Quantitative Forecasting Approaches in Finance:

These are some key approaches:

(1) Average Forecasting Approach:

Assumes that the course of future market data will be equal to the statistical mean of past market data.

(2) Naive Forecasting Approach:

Assumes that the time series follow certain seasonality. This approach is particularly useful for financial time series, which include patterns difficult to recognize and predict. Naive Forecasting is often used as a benchmark, against which other forecasting models can be compared and filtered.

(3) Drift Forecasting Approach:

It is a more sophisticated variation of the Naive Approach. It includes an increase or decrease over time, which is called a drift.

(2) Fundamental Analysis

Fundamental analysis is a method of pricing a Financial Instrument by examining and evaluating all related internal and external factors. These factors include financial, economic, social, political, strategic, and other quantitative and qualitative variables. The fundamental analysis aims to produce a 'fair value' that can be compared with the current price of the market.

Fundamental Analysis uses a broad range of real data (macroeconomic data, industry analysis, balance sheets, earnings reports, key data releases, etc) in the evaluation of a financial instrument's value.

The fundamental and the quantitative analyses are characterized by core differences, but at the same time, they share also many similarities. For example, when evaluating shares, both methods use market capitalization, sector classification, price/earnings ratio, dividend policy, etc. Therefore, quantitative models can be used for optimizing the outcomes of the fundamental analysis.

(3) Combining Quantitative Forecasting and Fundamental Analysis (Quantamental Analysis)

The Quantamental method is able to combine the massive power of computer algorithms with the human skill of being able to cope and adapt to new market conditions. Quantamental investors combine the ability to focus on certain market strengths and weaknesses (fundamental analysis) with the big data series and the computing skills of the quantitative method. In this way, they can focus anywhere on a broad market and test effectively their hypotheses.

Quantamental's analysis advantages include:

-1- Combining computer power with native human abilities

-2- Adapting fast to any new market conditions (a common phenomenon in finance)

-3- Testing any fundamental hypothesis using long historical data series

-4- Being able to combine the short and mid-term skills of the quantitative method with the long-run horizon of the fundamental analysis

-5- Filtering and arranging any new recognizable pattern

-6- Taking advantage of the massive data available on the internet

-7- Achieving innovation, and at the same time, data optimization

-8- Differentiating results against other investors using common fundamental models

The Quantamental approach is becoming very popular based on today's availability of alternative data. Alternative data include data from social media and broader from our new cyber world.

StrategyQuant: Trading 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 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 is 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).

These are some key features of the platform:

• Find easily thousands of automated strategies trading currencies, stocks, derivatives, etc.
• Multi-currency backtesting
• 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 backtesting and optimization modules (External tick-data support)

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