ARGUS Enterprise 了解您的投資組合,可以控制風險並提高您獲利!
ARGUS Enterprise 整合了三大評估、資產管理解決方案,包含:ARGUS Valuation DCF、ARGUS Asset Management(舊名DYNA)與ARGUS Valuation - Capitalisation(舊名Circle),將其整合到單一綜合平台。 ARGUS產品汲取了豐富的經驗並結合大量的客戶反饋,增強了許多的功能,為客戶提供突破性的解決方案!
透過ARGUS產品您將可以結合專業知識、全球市場的知識、經驗和數據,透過正確的投資管理工具,您將可以迅速做出決策!
ARGUS Enterprise提供您認識和了解您的投資組合的詳細資料,讓您可以熟練地管理您的風險,讓您可以掌控自己的命運。
Unprecedented visibility into your portfolio
With advanced portfolio tools, create KPIs and dashboards customized for each user. Pivot portfolio results and analyze with filters and charts.
Intuitive and easy to use
New user interfaces for budgeting and other user interface enhancements make for faster analysis and less errors. Screens that are familiar to existing ARGUS users, allowing you to enter data the way you work.
Fastest deployment in the industry
ARGUS Enterprise can be installed at the desktop level for immediate property modeling or deployed on an enterprise level with enterprise level security and configuration. Our new deployment options will have you up and running as soon as you download and install the solution.
Interconnected System
Dynamic connections to Excel, combine with ARGUS Symphony to make ARGUS Enterprise a truly connected platform. Never before has the data been more accessible and usable bi-directionally with any asset management solution.
ARGUS Compatible
Leverage ARGUS DCF data. Along with the user interface, the data in ARGUS DCF files can be easily imported into ARGUS Enterprise. Trust ARGUS to create the highest level of compatibility with ARGUS DCF files.
ChaosHunter ®是一個獨立的軟體工具,旨在產生可讀的公式,您的數字數據模型的應用如下:
● 對金融市場生產買入/賣出信號
● 預測未來價值的時間序列,包括市場價格
● 建立科學數據模型
● 建立企業財務或銷售數據模型
● 預測實驗的結果等等
ChaosHunter要求您從電子表格或數據源輸入歷史文本文件或樣本數據。然後您需要選擇算術類別和您想使用的ChaosHunter的其他數學函數,之後它會產生的數值公式,讓您可以閱讀、理解、利用,甚至對外出售。ChaosHunter可用功能還包括神經網絡和混沌函數。
由ChaosHunter公式所產生的買入/賣出信號,可以被轉移到許多流行的交易平台,它使您能夠將您的模型與貿易數字的經紀公司。我們現在可以利用已經開發完成的界面,配合以下的平台就能讓您更輕鬆轉換ChaosHunter模型:
● NeuroShell® Trader Professional
● NeuroShell® DayTrader Professional
● Interactive Brokers Trader Workstation
● TradeStation®
● NinjaTrader®
● MetaTrader 4
● Wealth-Lab Pro®
● eSignal®
● Microsoft® Excel
Science and Trade Modeling
The ChaosHunter works by evolving formulas from basic building blocks - "atomic" functions like add, subtract, multiply, divide, sine, cosine, square root, etc. The user selects which of these functions will be in the pool of available functions, and the ChaosHunter evolves combinations that continually get better at solving the problem.
Solving the problem can mean predicting or classifying a time series for business and science users (primarily), or it can mean generating buy/sell signals for trading. Since the formulas that get generated are usually not esoteric like neural network formulas, you can show them to your boss, modify them, and insert them into other programs.
The ChaosHunter's atomic function set contains simple polynomials (e.g. a2 + b2), neurons, boolean functions (AND, OR, NOT), and many more. The neuron functions (if chosen) can combine to form unique neural net structures.
For our science and business users, you get models that have readable, understandable formulas to model the data. If the data is time series, it builds recurrent formulas necessary for most definitions of chaotic functions. Although it mostly predicts, you can classify as well if there are two classes, one of which can be described by positive numbers and the other by negative numbers.
The trading models it builds can be used standalone or in conjunction with NeuroShell Trader Professional or DayTrader Professional, and they are capable of investigating chaotic time series.
Traders can also fire the trading models the ChaosHunter makes from any number of trading products that you may have purchased before you found out how good NeuroShell Trader Professional is. The reason is that ChaosHunter makes formulas that can most likely be inserted into many of these systems. So you build the model (formula) based on text files exported from NeuroShell Trader or these other systems, then use the model in real time somewhere else. Of course, we have a much easier way to insert the models into NeuroShell...
Data Mining with Cubist
Cubist 資料採礦軟體工具
資料探勘(Data mining)是指從組織的資料庫中萃取資料的過程,這些資料通常被用來洞察該組織的營運模式和預設未發生之結果,以支援使用者做決策。
Cubist是 RuleQuest Research 公司開發的建立預測模型的工具。其內建的規則算法可幫助建立預測模型的輸出值,並與See5/C5.0產品互補。例如,See5/C5.0可能依據其百分比將數據分類為“高”、“中等”或“低”,而Cubist將會是輸出一個數字,如“7.3”。
Cubist是一個功能強大的工具,Cubist模型比那些一般的技術,如多元線性回歸得可以到更好的結果,同時也比神經網絡分析更容易理解。
DEA SolverPro
近年來已經有很多使用資料包絡分析(Data Envelopment Analysis, DEA)來評估許多不同種類實體效益的多種應用,在很多不同背景的國家中從事不同的活動。一個原因是,因為經常有未知的複雜因素,很多活動牽扯到多次輸入和輸出之間的關係,DEA已經提供對解決這些情況的可能性。例子包括:在不同地理位置的美國空軍基地的維護活動,英國和威爾斯的警力分配,賽普勒斯和加拿 大分行的效能,和美國、英國以及法國的大學實施他們的教育和研究過程中的效率。借著各式各樣的輸入和輸出(包括把"社會"和"安全網"的支出當作輸入數 據,把各種方面的"生活品質"當做輸出資料),這些種類的應用延伸到用來評價城市、地區和國家的實施效率。
SAITECH Inc. 公司的 DEA-Solver-Pro 除了整合SBM (Non-Oriented SBM ) 模型並加上了 “Global RTS” 模型,也針對前幾版的功能做了修正。該模型是一個整合的模型,如圖A所示,該模型可以評估(1)所觀察到的整個期間的整體效率,(2)動態變化的週期(長期)效率(3)部門效率的動態變化及(4)分區麥式(Malmquist)指數。
這些特徵通過統一徑向(unifying radial)和非徑向模型考慮了輸入/輸出資料的差異,以及他們對於測量技術效率的相對重要性。使用EBM模型,DEA-Solver-Pro變得非常有效,甚至是那些帶有依賴資料和相關資料的案例。
更新介紹
Version 16: with the newest release of version 16b, we introduce a new input style, called Desirable Inputs Model. In this new model, we allow some input style (called IGood) which are larger the better. Examples include number of electric vehicles in an environmental model, the number of test takers in vaccine development model, etc.
DTREG 是一款完美的建模工具,適用於商業建模或建立多種醫學資料模型,例如性別、人種、婚姻狀況等! 從一組數據值中提取有用信息的過程被稱為「數據挖掘」(data mining)。這些數據可以用來創建模型並做出預測。目前的技術已經發展出許多預測模型,而如何選擇和應用最好的模型則是一種藝術。
DTREG實現強大的已開發預測建模方法。你可以使用決策樹模型、支援向量機(Support Vector Machine, SVM)、基因表示規劃法(Gene Expression Programming)、符號回歸(Symbolic Regression),K-means 分群法(k-means clustering)、判別分析法(discriminant analysis)、線性回歸(Linear Regression models)和(Logistic Regression models)。DTREG也可以進行時間序列分析和預測。
Classification and Regression Trees. DTREG can build Classification Trees where the target variable being predicted is categorical and Regression Trees where the target variable is continuous like income or sales volume.
DTREG 提供多種強大的預測建模
Multilayer Perceptron Neural Networks
Probabilistic Neural Networks
General Regression Neural Networks
RBF Neural Networks
GMDH Polynomial Neural Networks
Cascade Correlation Neural Networks
Support Vector Machine (SVM)
Gene Expression Programming - Symbolic Regression
Decision Trees
TreeBoost — Boosted Decision Trees
Decision Tree Forests
K-Means Clustering
Linear Discriminant Analysis (LDA)
Linear Regression
Logistic Regression
Checking Data Quality with GritBot
使用GritBot檢查您的數據品質(Data Quality)
資料探勘(Data mining)是指從組織的資料庫中萃取有價值資料的技術。資料探勘的搜索模式對於分析資料的影響重大,如果分析的資料中包含錯誤的數值,其分析的資料將會是“垃圾進,垃圾出”(Garbage in, garbage out.)。
GritBot是 RuleQuest Research 公司開發的異常檢測工具,其作為自動的異常數據資料發現工具,其試圖在分析前找到資料中的異常數據。它可以被認為是一個獨立的資料品質審核員,尋找資料庫中異常的離散值或連續屬性的數值。使用GritBot可以提高See5/C5.0和Cubist等演算法從資料集構造模型的有效性。
Crystal Ball 是一套與工作表格完美結合的套裝軟體,運用蒙地卡羅模擬與各種模型做出預測分析與最優化解。Crystal Ball被廣泛的運用在各個不同的領域,例如財務分析、製造業、能源運用、醫療生技等。針對不同的用戶群,Crystal Ball擁有廣泛的應用功能,包括財務風險分析、資產評估、財務工程、六標準差、投資組合分析、成本預估、專案管理等。
Crystal Ball是一個容易使用的模擬程式,它可以在Microsoft Excel內幫助您分析風險和不確定的關聯。
Oracle Crystal Ball is the leading spreadsheet-based application suite for predictive modeling, forecasting, simulation, and optimization. It gives you unparalleled insight into the critical factors affecting risk. With Crystal Ball, you can make the right tactical decisions to reach your objectives and gain a competitive edge under even the most uncertain market conditions.