ForeStock is a predictor and indicator module that plugs into TradeStation, MetaStock, Ninja Trader and Excel.
ForeStock a number of highly adaptive cutting edge market predictors. In addition to predictors, ForeStock contains
a large number of specialized indicators suited for various market conditions.
This document discusses Erika's background, what she expects to learn from an education technology class, and how the class has affected her perception of teaching as a career. Erika is a 21-year-old student from Mapastepec who has lived in Tapacuhla for 3 years. She expects to learn new technologies for teaching English, like using websites and podcasting. The class has shown her the importance of using new technologies but has not changed her choice to become a teacher, which she finds more dynamic and engaging than other careers.
The document discusses Java Beans, Applets, JDBC, Networking in Java, JNDI, and some key classes used in these technologies. It provides an overview of concepts like Java Beans components, properties, events, introspection, customization, persistence. It describes the lifecycle and methods of Applets. It outlines the basic steps to use JDBC like loading drivers, establishing connections, executing queries. It discusses connection-oriented and connectionless networking in Java and common network classes like Socket, ServerSocket, URL, URLConnection. It provides a high-level overview of the JNDI architecture.
Precision Oscillator Suite for Bloomberg Professionalbzinchenko
We offer a simple and powerful collection of improved technical indicators inherited from classical oscillators widely used throughout modern technical analysis. These oscillators take advantage of full intra-bar information provided by the Bloomberg charting package. They allow for up to four times more precision against their classical counterparts.
This document discusses how StockFusion Studio provides access to market data and liquidity providers. It can connect to various real-time and end-of-day data feeds like DTN IQFeed, Yahoo Finance, MetaStock, and TC2000. It also allows importing data from text files, databases like Microsoft SQL Server, and custom data feeds. The software includes tools for selecting data sources, managing portfolios of stocks, searching for symbols, and exporting data in different formats.
The document discusses font choices for a film project. It notes that fonts should appeal to the film's genre and catch audience attention. Horror film magazines often use bold masthead fonts. The document selects the "Feast of Flesh" font from dafont.com for its bold yet dark and urban style. It also chooses the "October Crow" font for the movie logo because it looks sinister and eye-catching in representing the horror genre. The document plans to use these fonts and start layout designs.
The document provides information about certification programs offered by e-Zsigma, including Lean, Six Sigma, and Lean Six Sigma. It highlights testimonials from past students about the benefits and impact of the programs. The dates and pricing are listed for upcoming certification programs in Lean, Six Sigma, and their combination in the fall of 2012.
BPM-Xchange™ offers an inimitable solution to the EA, BPA and BPM market to enable real tool integration, to tear down methodology and technology barriers, and to increase user productivity with overall less TCO and a unique ROI.
WSO2 Machine Learner takes data one step further, pairing data gathering and analytics with predictive intelligence: this helps you understand not just the present, but to predict scenarios and generate solutions for the future.
This document discusses Erika's background, what she expects to learn from an education technology class, and how the class has affected her perception of teaching as a career. Erika is a 21-year-old student from Mapastepec who has lived in Tapacuhla for 3 years. She expects to learn new technologies for teaching English, like using websites and podcasting. The class has shown her the importance of using new technologies but has not changed her choice to become a teacher, which she finds more dynamic and engaging than other careers.
The document discusses Java Beans, Applets, JDBC, Networking in Java, JNDI, and some key classes used in these technologies. It provides an overview of concepts like Java Beans components, properties, events, introspection, customization, persistence. It describes the lifecycle and methods of Applets. It outlines the basic steps to use JDBC like loading drivers, establishing connections, executing queries. It discusses connection-oriented and connectionless networking in Java and common network classes like Socket, ServerSocket, URL, URLConnection. It provides a high-level overview of the JNDI architecture.
Precision Oscillator Suite for Bloomberg Professionalbzinchenko
We offer a simple and powerful collection of improved technical indicators inherited from classical oscillators widely used throughout modern technical analysis. These oscillators take advantage of full intra-bar information provided by the Bloomberg charting package. They allow for up to four times more precision against their classical counterparts.
This document discusses how StockFusion Studio provides access to market data and liquidity providers. It can connect to various real-time and end-of-day data feeds like DTN IQFeed, Yahoo Finance, MetaStock, and TC2000. It also allows importing data from text files, databases like Microsoft SQL Server, and custom data feeds. The software includes tools for selecting data sources, managing portfolios of stocks, searching for symbols, and exporting data in different formats.
The document discusses font choices for a film project. It notes that fonts should appeal to the film's genre and catch audience attention. Horror film magazines often use bold masthead fonts. The document selects the "Feast of Flesh" font from dafont.com for its bold yet dark and urban style. It also chooses the "October Crow" font for the movie logo because it looks sinister and eye-catching in representing the horror genre. The document plans to use these fonts and start layout designs.
The document provides information about certification programs offered by e-Zsigma, including Lean, Six Sigma, and Lean Six Sigma. It highlights testimonials from past students about the benefits and impact of the programs. The dates and pricing are listed for upcoming certification programs in Lean, Six Sigma, and their combination in the fall of 2012.
BPM-Xchange™ offers an inimitable solution to the EA, BPA and BPM market to enable real tool integration, to tear down methodology and technology barriers, and to increase user productivity with overall less TCO and a unique ROI.
WSO2 Machine Learner takes data one step further, pairing data gathering and analytics with predictive intelligence: this helps you understand not just the present, but to predict scenarios and generate solutions for the future.
Stock Market Prediction using Machine LearningIRJET Journal
This document discusses using machine learning techniques to predict stock market movements. It reviews various machine learning algorithms that have been used for stock prediction, including support vector machines, recurrent neural networks, convolutional neural networks, random forests, and ARIMA/GARCH models. The document then focuses on using a deep learning algorithm with ANN and CNN models to predict stock market movements. It provides details on how different machine learning algorithms work, such as linear regression, logistic regression, k-means clustering and random forests. The goal is to develop a financial data predictor program to mitigate uncertainty in investment decision making.
IRJET- Stock Market Prediction using Machine LearningIRJET Journal
This document discusses using machine learning techniques to predict stock market movements. Specifically, it uses a Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to predict stock prices. It describes collecting stock price data, selecting features like price volatility and momentum, training the SVM model on historical data, and generating predictions of future stock prices. The results show the SVM model was able to accurately predict the movements of IBM stock prices based on historical data.
This document discusses reactive systems and programming. It begins with an introduction to reactive systems and programming, explaining the difference between the two. It then discusses why reactive systems are useful, covering topics like efficient resource utilization. The document goes on to explain key concepts like observables, backpressure, and reactive libraries. It provides examples of reactive programming with Spring Reactor and reactive data access with Couchbase. Overall, the document provides a high-level overview of reactive systems and programming concepts.
This document describes market forecasting algorithms provided by Quant Trade Technologies, including self-optimizing ARIMA, finite impulse response neural networks, finite state Markov automation, stepwise best regression, square root regression, logistic regression, and more. It explains the mathematical models behind various linear and nonlinear regression techniques for time series forecasting and analyzing financial market data.
Fractal Suite Trading Indicators for Bloomberg Professionalbzinchenko
The document describes the Fractal Trading Suite, which uses fractal analysis, artificial intelligence, and other predictive tools to forecast market movements. It contains predictors for price closes, highs, and lows. Other tools include an Attractor-Repulsor Coefficient that measures how close the current price is to historical prices, indicator flags to show predictor health, and oscillators for identifying entry and exit signals. The suite aims to provide accurate short-term forecasts for different trading styles.
NAG software for the Actuarial Community (Sep. 2012)John Holden
The document discusses numerical software and tools from NAG for the actuarial community. It provides an overview of NAG, including the types of numerical libraries and toolboxes it offers. It also discusses why numerical computation is important and challenging, and how software providers rely on libraries like NAG rather than writing all numerical code themselves. Actuarial problems that can benefit from NAG libraries are also highlighted.
This document discusses alerts in EMC Documentum xCelerated Composition Platform Version 2.1. It defines key concepts like alert definitions, instances, and queries. It explains how to create and configure alerts using the xCP Designer by setting triggers, datasets, actions, and queries. A sample historical query and alert definition for tracking quarterly loan amounts is provided as an example.
The document discusses attention mechanisms and their implementation in TensorFlow. It begins with an overview of attention mechanisms and their use in neural machine translation. It then reviews the code implementation of an attention mechanism for neural machine translation from English to French using TensorFlow. Finally, it briefly discusses pointer networks, an attention mechanism variant, and code implementation of pointer networks for solving sorting problems.
Sochi hexitex manchester 10 dec 2008 presentationTaha Sochi
The document describes EasyEDD, a software for processing powder diffraction data from tomographic energy-dispersive diffraction (TEDDI) experiments. EasyEDD allows batch processing of large quantities of TEDDI data through a graphical user interface. It supports common data formats and provides tools for data correction, visualization as color-coded grids, fitting of diffraction patterns, and analysis of results. The software combines these capabilities into an integrated environment to facilitate the analysis of data from high throughput TEDDI detectors.
Design the implementation of 1D Kalman Filter Encoder and Accelerometer.Ankita Tiwari
1) The document describes an experiment using LabVIEW to implement a 1D Kalman filter encoder and accelerometer on a robot. LabVIEW is a visual programming language that uses graphical programming techniques instead of text.
2) The experiment uses various LabVIEW VIs (virtual instruments) including ones for reading simulated LIDAR sensor data, applying a vector field histogram algorithm to identify obstacles, and applying velocity controls to move the robot.
3) Precautions are noted such as configuring all events in a single event structure to avoid locking up the user interface.
IRJET - Stock Market Prediction using Machine Learning AlgorithmIRJET Journal
This document discusses using machine learning algorithms to predict stock market prices. Specifically, it analyzes using Support Vector Machine (SVM) and linear regression (LR) algorithms to predict stock prices. It finds that linear regression provides more accurate predictions than SVM when tested on the same stock data. The methodology trains models on historical stock data using these algorithms and predicts future prices, achieving up to 98% accuracy when testing linear regression predictions on Google stock prices. It concludes that input data and machine learning techniques can effectively predict stock market movements.
Tool wear monitoring and alarm system based on pattern recognition with logic...Nehem Tudu
This document contains a summary of 14 core seminars presented by Nehem Tudu on tool wear monitoring and alarm systems based on pattern recognition with logical analysis of data (LAD). The seminars covered topics such as tool wear, LAD methodology, experimental design, knowledge extraction using LAD, and developing a proportional hazards model. The goal was to use data from machining titanium alloy to train LAD models to automatically detect worn tool patterns and build an online tool wear alarm system without human interference. LAD was able to accurately classify observations with a quality of 97.2%.
Thierry Bema is a 44-year-old French national with experience in data science and enterprise business intelligence projects using technologies like Spark, Hadoop, and Scala. He has created multinode clusters on Hadoop and run various financial market and machine learning applications including stock price prediction, portfolio risk calculation, and sentiment analysis. His background also includes 17 years of experience designing RFICs for wireless technologies.
Thierry Bema is a 44-year-old French national with experience in data science and enterprise business intelligence projects using technologies like Spark, Hadoop, and Scala. He has created multinode clusters on Hadoop and run various financial market and machine learning applications including stock price prediction, portfolio risk calculation, and sentiment analysis. His background also includes 17 years of experience designing RFICs for wireless technologies.
This document provides an overview of an embedded systems project to create a collision avoidance robot. It discusses the components of the robot including sensors to detect obstacles, a microcontroller to process sensor signals and control movement, and a motor to move the robot forward and backward. The document also describes the software used to program the microcontroller and provides sample code to control the robot's movement based on sensor readings.
Amit Bhandari has over 9 years of experience in software development using technologies like Java, Oracle, C++ and Visual Basic. He has expertise in all phases of the SDLC from requirements analysis to delivery. Some of his projects include developing a file search utility using C++ and Boost library, a market watch tool, and applications for reconciliation reporting and risk calculation. He is proficient in software design, development, testing and optimization.
The document discusses a new approach to OpenStack automation called Group-Based Policy (GBP). GBP aims to capture an application's infrastructure needs at a higher level of abstraction, independent of the underlying implementation details. It introduces several new concepts, including groups to organize resources, traffic classifiers to define network traffic, and policy tags to apply governance rules. The goal is for applications to simply describe their requirements and dependencies rather than having to specify low-level configuration details.
design the implementation of trajectory path of the robot using parallel loopAnkita Tiwari
This document summarizes an experiment using LabVIEW to implement the trajectory path of a robot using a parallel loop algorithm. It uses LabVIEW to create a control loop with sub-VIs for steering, reading LIDAR sensor data, vector field histogram analysis, and applying velocity to wheels. The experiment executes timed loops to process sensor data and control the robot at 10 Hz while avoiding obstacles identified by the vector field histogram analysis.
AlgoB – Cryptocurrency price prediction system using LSTMIRJET Journal
This document describes a cryptocurrency price prediction system called AlgoB that uses an LSTM neural network model. The system was developed by four students to predict cryptocurrency prices with high accuracy. It takes historical price data as input and can predict future prices. The system uses libraries like NumPy, Pandas, TensorFlow and Matplotlib. It achieves 80% prediction accuracy, outperforming regression and tree models. The LSTM model is trained on price data and evaluates predictions against real prices. This helps traders understand market movements and identify good times to buy and sell cryptocurrencies.
ClearTH Test Automation Framework: Case Study in IRS & CDS Swaps Lifecycle Mo...Iosif Itkin
Synchronize Europe
18th June 2019
Iosif Itkin, co-CEO and co-founder, Exactpro
Using the ISDA CDM Swaps application, simultaneously execute multiple end-to-end scenarios for DAML applications in capital markets - validate with actual contract data on ledger.
The document discusses the risks of commodity futures trading, securities trading, and hypothetical performance results. It states that trading commodity futures and securities can result in total loss. It also notes that hypothetical performance results have limitations and do not account for financial risk. Past performance is not indicative of future results.
The document describes the user account registration and login procedure for the StockFusion Studio platform. It outlines the steps to register for a new account, including downloading the platform, filling out the registration form, confirming the account via email, and logging in with username and password. It also provides information on connectivity issues, firewall rules, and contacting support.
Stock Market Prediction using Machine LearningIRJET Journal
This document discusses using machine learning techniques to predict stock market movements. It reviews various machine learning algorithms that have been used for stock prediction, including support vector machines, recurrent neural networks, convolutional neural networks, random forests, and ARIMA/GARCH models. The document then focuses on using a deep learning algorithm with ANN and CNN models to predict stock market movements. It provides details on how different machine learning algorithms work, such as linear regression, logistic regression, k-means clustering and random forests. The goal is to develop a financial data predictor program to mitigate uncertainty in investment decision making.
IRJET- Stock Market Prediction using Machine LearningIRJET Journal
This document discusses using machine learning techniques to predict stock market movements. Specifically, it uses a Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to predict stock prices. It describes collecting stock price data, selecting features like price volatility and momentum, training the SVM model on historical data, and generating predictions of future stock prices. The results show the SVM model was able to accurately predict the movements of IBM stock prices based on historical data.
This document discusses reactive systems and programming. It begins with an introduction to reactive systems and programming, explaining the difference between the two. It then discusses why reactive systems are useful, covering topics like efficient resource utilization. The document goes on to explain key concepts like observables, backpressure, and reactive libraries. It provides examples of reactive programming with Spring Reactor and reactive data access with Couchbase. Overall, the document provides a high-level overview of reactive systems and programming concepts.
This document describes market forecasting algorithms provided by Quant Trade Technologies, including self-optimizing ARIMA, finite impulse response neural networks, finite state Markov automation, stepwise best regression, square root regression, logistic regression, and more. It explains the mathematical models behind various linear and nonlinear regression techniques for time series forecasting and analyzing financial market data.
Fractal Suite Trading Indicators for Bloomberg Professionalbzinchenko
The document describes the Fractal Trading Suite, which uses fractal analysis, artificial intelligence, and other predictive tools to forecast market movements. It contains predictors for price closes, highs, and lows. Other tools include an Attractor-Repulsor Coefficient that measures how close the current price is to historical prices, indicator flags to show predictor health, and oscillators for identifying entry and exit signals. The suite aims to provide accurate short-term forecasts for different trading styles.
NAG software for the Actuarial Community (Sep. 2012)John Holden
The document discusses numerical software and tools from NAG for the actuarial community. It provides an overview of NAG, including the types of numerical libraries and toolboxes it offers. It also discusses why numerical computation is important and challenging, and how software providers rely on libraries like NAG rather than writing all numerical code themselves. Actuarial problems that can benefit from NAG libraries are also highlighted.
This document discusses alerts in EMC Documentum xCelerated Composition Platform Version 2.1. It defines key concepts like alert definitions, instances, and queries. It explains how to create and configure alerts using the xCP Designer by setting triggers, datasets, actions, and queries. A sample historical query and alert definition for tracking quarterly loan amounts is provided as an example.
The document discusses attention mechanisms and their implementation in TensorFlow. It begins with an overview of attention mechanisms and their use in neural machine translation. It then reviews the code implementation of an attention mechanism for neural machine translation from English to French using TensorFlow. Finally, it briefly discusses pointer networks, an attention mechanism variant, and code implementation of pointer networks for solving sorting problems.
Sochi hexitex manchester 10 dec 2008 presentationTaha Sochi
The document describes EasyEDD, a software for processing powder diffraction data from tomographic energy-dispersive diffraction (TEDDI) experiments. EasyEDD allows batch processing of large quantities of TEDDI data through a graphical user interface. It supports common data formats and provides tools for data correction, visualization as color-coded grids, fitting of diffraction patterns, and analysis of results. The software combines these capabilities into an integrated environment to facilitate the analysis of data from high throughput TEDDI detectors.
Design the implementation of 1D Kalman Filter Encoder and Accelerometer.Ankita Tiwari
1) The document describes an experiment using LabVIEW to implement a 1D Kalman filter encoder and accelerometer on a robot. LabVIEW is a visual programming language that uses graphical programming techniques instead of text.
2) The experiment uses various LabVIEW VIs (virtual instruments) including ones for reading simulated LIDAR sensor data, applying a vector field histogram algorithm to identify obstacles, and applying velocity controls to move the robot.
3) Precautions are noted such as configuring all events in a single event structure to avoid locking up the user interface.
IRJET - Stock Market Prediction using Machine Learning AlgorithmIRJET Journal
This document discusses using machine learning algorithms to predict stock market prices. Specifically, it analyzes using Support Vector Machine (SVM) and linear regression (LR) algorithms to predict stock prices. It finds that linear regression provides more accurate predictions than SVM when tested on the same stock data. The methodology trains models on historical stock data using these algorithms and predicts future prices, achieving up to 98% accuracy when testing linear regression predictions on Google stock prices. It concludes that input data and machine learning techniques can effectively predict stock market movements.
Tool wear monitoring and alarm system based on pattern recognition with logic...Nehem Tudu
This document contains a summary of 14 core seminars presented by Nehem Tudu on tool wear monitoring and alarm systems based on pattern recognition with logical analysis of data (LAD). The seminars covered topics such as tool wear, LAD methodology, experimental design, knowledge extraction using LAD, and developing a proportional hazards model. The goal was to use data from machining titanium alloy to train LAD models to automatically detect worn tool patterns and build an online tool wear alarm system without human interference. LAD was able to accurately classify observations with a quality of 97.2%.
Thierry Bema is a 44-year-old French national with experience in data science and enterprise business intelligence projects using technologies like Spark, Hadoop, and Scala. He has created multinode clusters on Hadoop and run various financial market and machine learning applications including stock price prediction, portfolio risk calculation, and sentiment analysis. His background also includes 17 years of experience designing RFICs for wireless technologies.
Thierry Bema is a 44-year-old French national with experience in data science and enterprise business intelligence projects using technologies like Spark, Hadoop, and Scala. He has created multinode clusters on Hadoop and run various financial market and machine learning applications including stock price prediction, portfolio risk calculation, and sentiment analysis. His background also includes 17 years of experience designing RFICs for wireless technologies.
This document provides an overview of an embedded systems project to create a collision avoidance robot. It discusses the components of the robot including sensors to detect obstacles, a microcontroller to process sensor signals and control movement, and a motor to move the robot forward and backward. The document also describes the software used to program the microcontroller and provides sample code to control the robot's movement based on sensor readings.
Amit Bhandari has over 9 years of experience in software development using technologies like Java, Oracle, C++ and Visual Basic. He has expertise in all phases of the SDLC from requirements analysis to delivery. Some of his projects include developing a file search utility using C++ and Boost library, a market watch tool, and applications for reconciliation reporting and risk calculation. He is proficient in software design, development, testing and optimization.
The document discusses a new approach to OpenStack automation called Group-Based Policy (GBP). GBP aims to capture an application's infrastructure needs at a higher level of abstraction, independent of the underlying implementation details. It introduces several new concepts, including groups to organize resources, traffic classifiers to define network traffic, and policy tags to apply governance rules. The goal is for applications to simply describe their requirements and dependencies rather than having to specify low-level configuration details.
design the implementation of trajectory path of the robot using parallel loopAnkita Tiwari
This document summarizes an experiment using LabVIEW to implement the trajectory path of a robot using a parallel loop algorithm. It uses LabVIEW to create a control loop with sub-VIs for steering, reading LIDAR sensor data, vector field histogram analysis, and applying velocity to wheels. The experiment executes timed loops to process sensor data and control the robot at 10 Hz while avoiding obstacles identified by the vector field histogram analysis.
AlgoB – Cryptocurrency price prediction system using LSTMIRJET Journal
This document describes a cryptocurrency price prediction system called AlgoB that uses an LSTM neural network model. The system was developed by four students to predict cryptocurrency prices with high accuracy. It takes historical price data as input and can predict future prices. The system uses libraries like NumPy, Pandas, TensorFlow and Matplotlib. It achieves 80% prediction accuracy, outperforming regression and tree models. The LSTM model is trained on price data and evaluates predictions against real prices. This helps traders understand market movements and identify good times to buy and sell cryptocurrencies.
ClearTH Test Automation Framework: Case Study in IRS & CDS Swaps Lifecycle Mo...Iosif Itkin
Synchronize Europe
18th June 2019
Iosif Itkin, co-CEO and co-founder, Exactpro
Using the ISDA CDM Swaps application, simultaneously execute multiple end-to-end scenarios for DAML applications in capital markets - validate with actual contract data on ledger.
The document discusses the risks of commodity futures trading, securities trading, and hypothetical performance results. It states that trading commodity futures and securities can result in total loss. It also notes that hypothetical performance results have limitations and do not account for financial risk. Past performance is not indicative of future results.
The document describes the user account registration and login procedure for the StockFusion Studio platform. It outlines the steps to register for a new account, including downloading the platform, filling out the registration form, confirming the account via email, and logging in with username and password. It also provides information on connectivity issues, firewall rules, and contacting support.
StockFusion Studio is software that provides tools for algorithmic trading strategies including backtesting, optimization, and portfolio analysis. It allows users to develop and test trading strategies using various forecasting algorithms and trading scripts. Key features include a backtesting engine, expert system for optimizing strategies, and tools for analyzing correlated symbols within a portfolio. The software also includes a scripting environment where users can develop custom trading strategies.
This document discusses broker connectivity and trade execution using StockFusion Studio. It provides information on installing brokerage software, setting up broker connections, order processing, and automatic trading. It describes how to set up supported brokers like TD Ameritrade, Interactive Brokers, MB Trading, and Trading Technologies. It also covers connecting to brokers, managing orders through the order book, and enabling automatic trading through expert advisors.
This document provides an overview of advanced charting and technical analysis features available in StockFusion Studio. It covers topics such as opening charts, selecting different chart types including candlestick, bar, and line charts. Additionally, it discusses chart customization options like changing color palettes and time periods. The document also explores incorporating studies like moving averages and Fibonacci analysis on charts. Finally, it addresses using over 100 technical indicators to analyze price data and generate trading signals.
The document describes StockFusion Studio, an intelligent trading expert adviser and trade execution platform. It provides capabilities such as quick start panels, data management, historical data, live charts, technical analysis, backtesting expert advisers, trade scripting, automatic trading, and portfolio analysis to help users develop and automatically execute trading strategies. It also allows connection to major brokers and full control over trade execution.
The document describes various algorithms for market forecasting provided by StockFusion Studio, including ARIMA, neural networks, Markov models, regression techniques, and other predictive models. It provides details on self-optimizing ARIMA, finite impulse response neural networks, finite state Markov automation, stepwise best regression, square root regression, logistic regression, and other algorithms. The goal is to select the best algorithms for time series forecasting and predicting future market states and stock prices.
This document describes the Samurai Suite trading software. It provides quad precision technical indicators and analysis tools that use all intra-bar price data (open, high, low, close) to generate trading signals. This results in indicators with up to 4x more precision than standard indicators. Key features include precise classical technical indicators, stable price aggregates using multiple data points, stochastic price beams showing probability distributions, and tools to take advantage of quantum uncertainty in markets. Comparisons show the precise indicators generate narrower price channels and more accurate signals than standard indicators.
The Simple Truth Behind Managed Futures & Chaos Cruncher
What is a Futures Contract?
What are Managed Futures?
Growth of Managed Futures?
BTOP50 Under Crisis
Robust Diversification
So Why Do Managers Use Futures?
Managed Futures Reduce Risk
Futures Markets are not a Casino
Hedging A Stock Portfolio
Algorithmic or “Systems” Trading
Why “Quant Trade” Uses Chaos Theory and Fractals in Trading
Efficient verses the Fractal Market Hypothesis
Fractal Attractors
Chaos Cruncher
Portfolio Scalability
Managed futures involve professional money managers investing in futures contracts across various markets like energy, agriculture, currencies, and equities using techniques like fundamentals analysis, technical analysis, arbitrage, or algorithms. A study found that including a managed futures index in a portfolio increased returns and reduced risk compared to only including stocks. Managed futures provide diversification benefits and can hedge against various economic risks due to investing across global markets and using different strategies.
Chaos Cruncher is the most advanced iteration of an automatic trading system designed, developed and used by Quant Trade. As our leading trading system, we have devised a way to offer it to our clients as a system service, in our Commodity Trading Advisor, or as a desktop application.
This document summarizes Quant Trader, a software for automated quantitative trading. It allows connecting to various brokers like TD Ameritrade and Interactive Brokers to place trades. The software provides tools for automatic trading using expert advisors on charts, and manages orders across multiple brokerage accounts in a centralized order book. Settings can be configured for broker connections, order types, and parameters for algorithmic trading.
This document describes Quant Trader, a software for algorithmic trading. It includes features for market forecasting, backtesting trading strategies, optimizing strategies, and portfolio analysis. Traders can use built-in algorithms and trading strategies, or create custom strategies using the scripting interface. The software aims to help traders develop and evaluate automated trading systems.
This document describes the market data and liquidity providers available in the Quant Trader software. It includes real-time market data feeds like DTN IQFeed and end-of-day data sources like Yahoo Finance. It also describes how to access this market data through the software's data access ribbon and select data sources. The software allows importing and exporting market data to databases and file formats like Microsoft SQL Server, text files, and PDF.
This document provides an overview of the features and capabilities of Quant Trader, an intelligent expert adviser and trade execution platform developed by Quant Trade Technologies. The platform includes tools for data management, historical data analysis, live charting, technical analysis, backtesting trades, creating automated trading strategies, and portfolio analysis. It also allows for automatic trading directly through major brokers and provides full control over trade execution.
Backtesting Engine for Trading Strategiesbzinchenko
Quant Trade Backtesting engine is the universal standalone software suitable to test performance of any recorded sequence of trades. It allows for independent evaluation of trading performance, calculation of trading statistics and visual representation of trading performance charts.
Fractal Forecasting of Financial Markets with Fraclet Algorithmbzinchenko
The document presents an overview of the Fraclet Predictor method for time series forecasting. It uses fractal dimensions to automatically select optimal parameters, including the lag length and number of nearest neighbors, for predicting future values in a time series. This non-linear method is evaluated on several examples, including logistic, Lorenz, and laser data, showing it can accurately forecast future points while setting parameters in linear time based on the fractal properties of the time series data.
Fractal Trading Strategies for Bloomberg Professionalbzinchenko
This document describes fractal trading strategies provided by Quant Trade Technologies. It includes four main strategies: No Mode, Trend Mode, Range Mode, and Fast Forward Mode. Each strategy can be customized with different parameters, indicators, filters and exits. The strategies can be backtested using the backtesting engine to analyze performance on historical market data.
Discover timeless style with the 2022 Vintage Roman Numerals Men's Ring. Crafted from premium stainless steel, this 6mm wide ring embodies elegance and durability. Perfect as a gift, it seamlessly blends classic Roman numeral detailing with modern sophistication, making it an ideal accessory for any occasion.
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How MJ Global Leads the Packaging Industry.pdfMJ Global
MJ Global's success in staying ahead of the curve in the packaging industry is a testament to its dedication to innovation, sustainability, and customer-centricity. By embracing technological advancements, leading in eco-friendly solutions, collaborating with industry leaders, and adapting to evolving consumer preferences, MJ Global continues to set new standards in the packaging sector.
Industrial Tech SW: Category Renewal and CreationChristian Dahlen
Every industrial revolution has created a new set of categories and a new set of players.
Multiple new technologies have emerged, but Samsara and C3.ai are only two companies which have gone public so far.
Manufacturing startups constitute the largest pipeline share of unicorns and IPO candidates in the SF Bay Area, and software startups dominate in Germany.
The 10 Most Influential Leaders Guiding Corporate Evolution, 2024.pdfthesiliconleaders
In the recent edition, The 10 Most Influential Leaders Guiding Corporate Evolution, 2024, The Silicon Leaders magazine gladly features Dejan Štancer, President of the Global Chamber of Business Leaders (GCBL), along with other leaders.
Implicitly or explicitly all competing businesses employ a strategy to select a mix
of marketing resources. Formulating such competitive strategies fundamentally
involves recognizing relationships between elements of the marketing mix (e.g.,
price and product quality), as well as assessing competitive and market conditions
(i.e., industry structure in the language of economics).
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Tata Group Dials Taiwan for Its Chipmaking Ambition in Gujarat’s DholeraAvirahi City Dholera
The Tata Group, a titan of Indian industry, is making waves with its advanced talks with Taiwanese chipmakers Powerchip Semiconductor Manufacturing Corporation (PSMC) and UMC Group. The goal? Establishing a cutting-edge semiconductor fabrication unit (fab) in Dholera, Gujarat. This isn’t just any project; it’s a potential game changer for India’s chipmaking aspirations and a boon for investors seeking promising residential projects in dholera sir.
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by Boris G. Zinchenko, Ph.D.
October 2010