AGRI Commodity Trading-Brokerage System
The AGRI Trading Module ("AGRI") is a Big Data cum Artificial Intelligence application "in-the-making" for Exporters and Domestically-Focused Traders of soybean and its derivatives, corn, wheat, etc. It feeds on users' capability in bringing crucial business data to its data warehouse for faster decision-making, integrated with the areas of Origination, Execution, Trading, Risk Management, Chartering, Controllership, Accounting, and Finance.
The Execution Module handles all processes in the execution of a contract, regardless if it is soybean, soy bran, soybean or sunflower oil, corn, or wheat. Through this module, you can define shipping periods, deliveries without shipment (no ship, barge, etc.), deliveries with the shipment, wash-out, string, circle, bank assignments, the nomination of contract, invoice generation/ printing. All this information can be integrated with Accounting and Finance departments, as well as with other departments in your business. You can price your contracts per each component (Port premium, futures, calendar spreads, etc.) or by its flat value.
The System may be deployed at a centralized location and distributed to all CTs or locally hosted within the trading office or a hybrid of a local setup with synchronization to a centralized location to manage the processes of new businesses' entry, contract settlement, and Long & Short. Through this System, CTs can define the entire range of information that will serve as a basis for the execution of contracts and risk analysis.
All in-and-out data is accumulated in the data warehouse, and the AI driven system sits on top of this data architecture or infrastructure. It can be accessed accordingly and appropriate in a secured manner to facilitate the exchange of commodity-related information across multiple points of global interaction in a centralized environment. This will result in global integrated electronic commodity trading system based on common enterprise architecture, data standards and privacy and security guidelines.
Hence it is appropriate for intelligent Commodity Traders (CT) to subscribe as a joint owner of an integrated and automated trading platform, and reap the fruits of the exit strategy by listing the entire intellectual asset through an initial public offer (IPO).
AGRI User Interface Design
Understanding The Business Model
Commodity Broking firms, such as ACI, Majuko, etc. are Commodity Traders' (CTs) and or risk managers. They serve as a vital link between Buyer and Seller who may:
1. Seek protection against adverse price changes by initiating a position in the futures market as a temporary substitute for the sale or purchase of the actual commodity
2. Attempt to profit from anticipated commodity price changes yet do not usually own or use the underlying product
3. Attempt and or enabling parties to profit from temporary distortions or inconsistencies in price
The CT constantly alert clients to market demand and supply scenarios and consequent market dynamics. This includes international price volatility that will decide which way the client stands concerning their competitors.
CT and clients may or may not wish to take delivery of the traded commodities; rather, they seek to profit during the change in the market prices. For this, it is necessary to be aware of the movement in the market, and clients take positions largely under the leads given these CTs.
The financial compensation for the services of these CTs depends exclusively on the trade indulged in and not on the performance of the portfolio.
The Business of "Improve-Your-Position" and the Benefits
CT (Buyer/Seller) is agents of "improve-your-position" fair trade committed. When a trade is made for a specific shipment month, there is a commodity type, a price, a quantity, and a port. The price fluctuates from the day a trade contract is made through to Port Declaration, through to Vessel Nomination, through to Shipping instruction and up to Tender Instruction stage. Hence, during this period, the extent of the trade contract indulged range from the lowest SOLD price, in-between SOLD price all the way to the highest SOLD price.
Hence, CTs who sold low earlier are desirous to know who else could they sell higher to and or buy lower from. This is where the brokers acting as risk managers play their roles - get Buyers and Sellers to indulge in trades so that the different price positioning can benefit and or leverage trades sold at a lower price.
This is the mechanics of Wash-out and Circle. Its computation is the outcome of the cash settlement contract. In it, the figure reflects the difference between the initial futures price and the price of the underlying product at settlement.
Understanding the Implication of Washout & Circle
For every CT, the goal is to achieve as many Washout and Circles from its very own pool of trades indulged for a specific commodity, quantity, port and the shipment month itself.
Thereafter, they are expected to collaborate with their counterparts to exchange "Trade Contracts that matches" their remaining trade contracts to further strike more Washout and Circle.
The faster a washout and or circle is identified and agreed upon by the parties, PAYOUT follows and need not wait till the actual shipment date.
Critical Success Factor of Commodity Broking Firm
CSF to every CT lies in managing the processes of new businesses' entry, contract settlement, and Long & Short. Through AGRI smart technical analysis, i.e. Define -> measure -> analyze -> improve -> control to evoke realization and serve as a basis for the execution of contracts and risk analysis.
Using Machine Learning for Value Creation
AGRI power lies in the fact that the developed algorithms can learn patterns from data, instead of being explicitly programmed to indicate the future price likelihood over a defined period. It uses X Days Historical Data - Current Price ($)- volume per day ($) - market cap ($) - volatility (%) - $1hr - $6hr :: $12hr :: $18hr :: $24hr (%) - Vol1hr :: Vol6hr :: Vol12hr :: Vol18hr :: Vol24hr.
AGRI also include Key Events and or Factors to predict the future price likelihood over a defined period (example 1, 3, 5, 7 months). Such as:
a. Production related
b. Time of the year (seasonal)
c. Storage and transportation constraints
d. Economic demand, and supply-side related patterns
e. Policies
f. Monetary factors
g. Costs involved in storage
It also uses data analysis techniques to pursue two main objectives:
1. What price should we set if we want to make the trade contract in less than a month?
2. What is the fair price of this commodity, given the current state of the market, the period of the year, the competition, and other augmenting factors?
In other words, the more Buyers and Sellers indulges AGRI to link trade volume, the more proficient AGRI can be in leveraging CTs trade position(s). Thereby, the more relevant AGRI become to CTs as a competitive advantage to manage and or manipulate price volatility.
