Visual Analytics And Visual Representation Are Not The Same

People are often confused with the terms – Visual analytics and Visual Representations. They many times take both words for the same meaning – presenting a set of data into some kind of graphs which looks good to the naked eye. However deep down, ask an analyst and they will tell you that visual representation and visual analytics are two different arts.

Visual Representation is used to present the analyzed data. The representations directly show the output from the analysis and are of less help to drive the decision. The decision is already known with analytics already performed on data.

On the other hand, Visual analytics is an integrated approach that combines visualization, human factors, and data analysis. Visual analytics allows human direct interaction with the tool to produce insights and transform the raw data into actionable knowledge to support decision- and policy-making. It is possible to get representations using tools, but not interactive visual analytics visualizations which are custom made. Visual Analytics capitalizes on the combined strengths of human and machine analysis (computer graphics, machine learning) to provide a tool where alone human or machine has fallen short.

The Process

The enormous amount of data comes with a lot of quality issue where data would be of different types and from various sources. In fact, the focus is now shifting from structured data towards semi-structured and unstructured data. Visual Analytics combines the visual and cognitive intelligence of human analysts, such as pattern recognition or semantic interpretation, with machine intelligence, such as data transformation or rendering, to perform analytic tasks iteratively.

The first step involves the integration and cleansing of this heterogeneous data. The second step involves the extraction of valuable data from raw data. Next comes the most important part of developing a user interface based on human knowledge to do the analysis which uses the combination of artificial intelligence as a feedback loop and helps in reaching the conclusion and eventually the decision.   

If the methods used to come to conclusion are not correct, the decisions emerging from the analysis would not be fruitful. Visual analytics takes a leap step here by providing methods/user interfaces to examine the procedures using the feedback loop.  

Visual Analytics and Visual Representations
Visual Analytics and Visual Representations

In general, the following paradigm is used to process the data:

Analyze First – Show the Important – Zoom, Filter and Analyze Further – Details on Demand (from:  Keim D. A, Mansmann F, Schneidewind J, Thomas J, Ziegler H: Visual analytics: Scope and challenges. Visual Data Mining: 2008, S. 82.)

Areas of Application

Visual Analytics could be used in many domains. The more prominent use could be seen in

  1. Financial Analysis
  2. Physics and Astronomy
  3. Environment and Climate Change
  4. Retail Industry
  5. Network Security
  6. Document analysis
  7. Molecular Biology

Today’s era greatest challenge is to handle the massive data collections from different sources. This data could run into thousands of terabytes or even petabytes/exabytes. Most of this data is in a semi-structured or unstructured form which makes it highly difficult for only a human to analyze or only a computer algorithm to analyze.

E.g. In the financial industry a lot of data (mostly unstructured) is generated on a daily basis and many qualitative and quantitative measures can be observed through this data. Making sense of this data is complex due to numerous sources and amount of ever-changing incoming data. Automated text analysis could be coupled with human interaction and knowledge (domain specific) to analyze this enormous amount of data and reduce the noise within the datasets. Analyzing the stock behavior based on news and the relation to world events is one of the prominent behavioral science application areas. Tracking the buy-sell mechanism of the stocks including the options trading in which the temporal context plays an important role, could provide an insight into the future trend. By combining the interaction and visual mapping of automated processed world events, the user could be supported by the system in analyzing the ever-increasing text corpus.  

Another example where visual analytics could be fruitful is the monitoring of information flow between various systems used by financial firms. These products are very specific to the domain and perform specific tasks within the organization. However, there is an input of data which is required for these products to work. This data flows between different products (either from the same vendor or different vendor) through integration files. Sometimes, it could become cumbersome for an organization to replace an old system with a new one due to these integration issues. Visual analytic tools could provide the current state of the flow and could help in detecting the changes would be required while replacing the old system with a new system. It could help in analyzing which system would be impacted most based on the volume and type of data being integrated reducing the errors and minimizing the administrative and development expenses.

Visual analytics tools and techniques create an interactive view of data that reveals the patterns within it, enabling to draw conclusions. At Magic FinServ, we deliver the intelligence and insights from the data and strengthen the decision making. Data service team from Magic would create more value for your organization by improving decision making using various innovative tools and approaches.

Magic also partners with top data solution vendors to ensure that your business gets the solution that fits your requirements, this way we rightly combine the technical expertise with business domain expertise to deliver greater value to your business. Contact us today and our team will be happy to speak with you for any queries.

Trading System And Algorithmic Trading Strategies

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What is a Trading System?

A “trading system” creates a set of trading strategies which are applied to the given input data to generate entry and exit signals (buy/sell) in a trading platform.The traders/professionals who create the trading strategies to maximize the profit are called “Quants”. They use exhaustive quantitative research & analysis to build such efficient strategies by applying advanced statistical and mathematical models.

Algorithmic trading – Algorithmic trading uses various algorithms to create a trading strategy from trading ideas. The algorithms are backtested with historical data and then used with real market data to give the best return. The execution can be done manually or automated.

Quantitative trading – Advanced mathematical and statistical models are used in Quantitative trading creation and execution of trading strategies.

Automated trading – Automated trading involves automated order generation, submission, and the order execution process. However, they are not fully automated. Manual interventions are also required

HFT (high-frequency) trading – Trading strategies can be classified into low-frequency, medium-frequency and high-frequency strategies as per the holding time of the trades. High-Frequency Trading strategy holds the trading position for a fraction of a second time and executes the trading strategy automatically. Millions of trades are an executed per day in this model.

The most of the algo-trading is high-frequency trading (HFT), which attempts to capitalize on placing a large number of orders at very fast speeds across multiple markets and multiple decision parameters, based on pre-programmed instructions.

The other name of Algo Trading is black box trading.

The profit opportunities are higher in algo trading and it makes markets more liquid and makes trading more systematic by ruling out emotional human impacts on trading activities via sentiment analysis.

Algorithmic Trading Strategies

  • Momentum/Trend Following:
    Calculate 50 days SMA (Simple Moving Average)
    Calculate 200 days SMA
    Take a long position when the 50 days SMA is larger than or equal to 200 days SMA
    Take a short position when the 50 days SMA is smaller than 200 day SMA. This is one of the most common algorithmic trading strategies. This follows trends in moving averages, channel breakouts, price level movements and related technical indicators. Algo Trader assumes there is a trend in the market and use the statistics to determine if the trend will continue. It does not involve making any predictions or price forecasts. Trades are initiated based on the occurrence of desirable trends. The above-mentioned example of 50 and a 200-day moving average is a popular trend following strategy.
  • Arbitrage Opportunities:
    Buying a dual listed stock at a lower price in one market and simultaneously selling it at a higher price in another market offers the price differential as risk-free profit or arbitrage. The same operation can be replicated for stocks versus futures instruments, as price differentials do exists from time to time. Implementing an algorithm to identify such price differentials and placing the orders allows profitable opportunities in an efficient manner. Also, trading can be triggered by the acquisition of the issuer company. This is called corporate event. Such event driven strategy is applied when the trader is planning to invest based on the pricing inefficiencies that may happen during a corporate event (before or after). Bankruptcy, acquisition, merger, spin-offs etc could be the event that drives such kind of an investment strategy. These strategies can be market neutral and used by hedge fund and proprietary traders widely. Index Fund Rebalancing: Index fund has defined periods of rebalancing to bring their holdings to par with their respective benchmark indices. This creates profitable opportunities for algorithmic traders, who capitalize on expected trades that offer 20-80 basis points profits depending upon the number of stocks in the index fund, just prior to index fund rebalancing. Such trades are initiated via algorithmic trading systems for timely execution and best prices.
  • Machine Learning based
    The major aspect of ML is learning from past data and predict the outcome of an unseen or new situation. The human learns in the same fashion however machine can process a huge volume of data much faster than human and predict the outcome. This is the way trading system works. Traders handle a large volume of historical data, analyze them and predict the stock price to establish a various trading strategy. Hence machine learning has become one of the key elements in Algo Trading system. There are many types of ML techniques depending on the nature of target prediction: Regression, Classification, Clustering, Association. The other set of categorization is Supervised (Target prediction is known to the model) vs Un-Supervised (Target prediction is unknown to the model) techniques. Python is a powerful language which supports statistical computations and can work with ML algorithms easily. R is another powerful language for statistical analysis.
  • Mathematical Model Based Strategies:( source Investopedia)
    A lot of proven mathematical models, like the delta-neutral trading strategy, which allows trading on a combination of options and its underlying security, where trades are placed to offset positive and negative deltas so that the portfolio delta is maintained at zero.
    • Trading Range (Mean Reversion):
      Mean reversion strategy is based on the idea that the high and low prices of an asset are a temporary phenomenon that reverts to their mean value periodically. Identifying and defining a price range and implementing an algorithm based on that allows trades to be placed automatically when the price of asset breaks in and out of its defined range.
    • Volume-Weighted Average Price (VWAP):
      The volume weighted average price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using stock specific historical volume profiles. The aim is to execute the order close to the Volume Weighted Average Price (VWAP), thereby benefiting on average price.
    • Time Weighted Average Price (TWAP):
      Time-weighted average price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using evenly divided time slots between a start and end time. The aim is to execute the order close to the average price between the start and end times, thereby minimizing market impact.
    • Percentage of Volume (POV):
      Until the trade order is fully filled, this algorithm continues sending partial orders, according to the defined participation ratio and according to the volume traded in the markets. The related “steps strategy” sends orders at a user-defined percentage of market volumes and increases or decreases this participation rate when the stock price reaches user-defined levels.
    • Implementation Shortfall:
      The implementation shortfall strategy aims at minimizing the execution cost of an order by trading off the real-time market, thereby saving on the cost of the order and benefiting from the opportunity cost of delayed execution. The strategy will increase the targeted participation rate when the stock price moves favorably and decrease it when the stock price moves adversely.

Benefits of Algorithmic Trading

  • Trades are executed timely and instantly to get benefit from best possible price change
  • Reduced risk of manual errors in placing the trades order and achieved higher performance
  • Reduced transaction costs
  • Take the benefit of multiple market conditions
  • Backtest the algorithm, based on available historical and real-time data
  • Reduced possibility of human error based on emotional and psychological factors of traders

Algo-trading is used in many forms of trading and investment activities, including:

  • Mid to long term investors or buy side firms (pension funds, mutual funds, insurance companies) who purchase in stocks in large quantities but do not want to influence stocks prices with discrete, large-volume investments.
  • Short term traders and sell side participants (market makers, speculators, and arbitrageurs) benefit from automated trade execution; in addition, algo-trading aids in creating sufficient liquidity for sellers in the market.
  • Systematic traders (trend followers, pairs traders, hedge funds, etc.) find it much more efficient to program their trading rules and let the program trade automatically.
  • Algorithmic trading provides a more systematic approach to active trading than methods based on a human trader’s intuition or instinct.