S AMBAR is a cloud-based service designed to give life to outdated digital financial articles and personalize them based on readers’ unique needs. Every day in the financial world, an enormous amount of resources are spent by publishers and authors to create digital content such as trading strategies and websites such as Amazon.com has 100s of books on this subject. Frequently, this content does not reach its intended audience, and even if it does, the end-user may not have the necessary knowledge, expertise, resources, and time to act on this information. Most of these articles have a very short lifespan, are boring to read, and are difficult to understand due to math and domain jargon. We solve this problem by Summarizing the article, Analyzing related data, and Visualizing the results in a graphical format for easy comperhension and analysis.
SAMBAR is currently analyzing and bringing to life thousands of outdated articles recommended in books, publications, financial exchanges and by experts over the years with trade recommendations and visualizations.
W e believe in operating under a win-win model by fostering mutually beneficial relationships with exchanges, publishers, authors, analysts, individuals and others alike. We act as a TaaS (Trades as a Service) analytical platform provider by assisting these groups to reach new markets and customers and helping them new revenue streams. We win by helping our customers and partners succeed.
t the heart of SAMBAR platform, we accumulate, transform and process massive amounts of data related to a specific financial instrument such as futures contracts and we detect patterns and relationships, study historical performance, formulate rules, validate new strategies, and visualize the information for easy understanding.
We are skilled at:
SAMBAR Trading Platform analyzes decades of financial data to create accurate projections of future trends. Our predictive analysis engine helps individual investors who are unfamiliar and daunted by the expanse of financial jargon by introducing them to a new landscape of opportunities. We create visualizations to communicate these strategies clearly and effectively through graphical means on the web, desktop, and on mobile devices.
Some commodities exhibit seasonality, which means predictable and repetitive behavior during certain times of the year produce less risky, profitable trading opportunities. For example, heating oil the primary heating source in the Northeast of the United States tend to increase seasonally from July to October in anticipation of upcoming winters. Likewise, many other commodities’ prices are subjected to seasonal trends.
These spreads capture the business economics of processing raw materials into end products. The soybean crush spread and the crude oil crack spread fall under this category. In the case of soybeans, the crusher buys the beans and processes them into soybean meal and soybean oil. For the crusher to make a profit has to sell the product output for more than the cost of beans. Similar economics play a role when refining crude oil into various output products.
Some spreads, at times, are out of their mean spread value and are likely to return to their mean value over time. When a spread deviates too far from its mean value, we can predict its possible direction in the future and act upon this trading opportunity. These spreads are range-bound and resemble a mean-reverting variable rather than a trending variable. A significant number of these opportunities occur between futures, ETFs, and ETNs.
Calendar spreads can be the mechanical process of balancing a nearby futures contract in a given a market with a deferred futures contract in the same market through a roll period until the nearby contract expires. These types of spreads exist in energy, grains and interest rate instruments such as Treasuries and Eurodollars. Crude oil calendar spreads fall under this category.
We collect, process and warehouse financial markets data every trading day. This data warehousing solution is built on multiple databases and provides easy and flexible access to large amounts of high quality and reliable stock, futures, and options data from exchanges around the world. We collect and store second-by-second data for thousands of stocks, futures contracts, and options contracts daily, and this effort requires substantial infrastructure, data modeling, technical resources and domain expertise.
We extract useful information from historical financial data sets to determine patterns—such as seasonal patterns in futures markets and forecast possible future outcomes and trends. We excel at extracting the Who, What and When aspects of data and connecting them to the Where.
We identify viable market trading strategies from various publications using our predictive analytics platform and the network of professionals. We also study these strategies and validate them using historical data before publishing them onto our platform. All strategies have to pass a strict internal benchmark on metrics, such as past performance, risk levels, liquidity, quotes, margin requirements and ROI to be considered for showcasing on the platform.
We ask the questions “How can we make someone comprehend the message regardless of his or her knowledge in the subject area?” or “How can we make a 10th-grader understand dividend stocks easily?”. The answer is Graphical Visualizations. We specialize in helping investors “see” patterns, trends, and correlations hidden in the sea of financial data, and make them understand the significance of trading these opportunities by building visual displays for easy comprehension.