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SUMMARY DATA

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Provide Order Flow Data

PROVIDERS

Provide Order

Flow Data

PROVIDERS

A1. Data Analysis : Order Flow Analysis

Opix Algo analyses the market microstructure by the order flow to obtain data of volume distribution and limit orders to determine the demand/supply-range of market makers. Makers’ order flows is highly informative and have the strongest predictive power for exchange rates and likely reflects key fundamental information. Their order flow has permanent forecasting power, whereas order flows originating from the other groups only predict transitory changes in exchange rates. These data can be transmitted to predict high volume areas to facilitate price discovery and develop reliable yield curves.

We believe that order flow is a valid proxy variable for private information and is often used in our research in the forex, equities, bonds and futures markets. Order flow analysis allows us to read the data below:

- Large executed orders
- Footprint patterns
- Volume & delta
- VPOC
- Limit orders waiting to be executed

A2. Data Analysis : Maker Warehouse Volume And Analytics

Market makers are individual participants or member firms of an exchange that helps create a market for investors to buy or sell securities. Traditionally, FX transactions between providers (‘makers’) and client have been conducted on either an agency or a principal basis. If providers act as a principal (‘maker’), they adding the trade into their own account and are exposed to market risks. For example, when traders (‘takers’) submit a 7 lots of market buy orders; the trades are supplied by makers. Hence, the makers are exposed to 7 lots of sell orders risk.

Since makers are risk averse, their objective is increasing in the expected value of their wealth while reducing volatility. When makers have a long position, buying additional orders is not attractive, as it increases the risk exposure; selling orders is attractive, as it reduces the exposure. Hence, makers are seeking to decumulate their position in a value price range.

This analysis of order flow and maker warehouse volume has enabled Opix Algo to ascertain the likelihood that makers will accumulate or decumulate inventory in order to fill trades within a given price range. By the same token, Opix Algo can predict the probability of high-volume supply at a given price range, allowing us to guarantee filled our trades at the lowest cost.

B1. Algorithm: Linear Regression Algorithm

Opix Algo predicts asset price changes based on the changes in microstructure by using linear regression models. It makes these predictions by first finding a set of coefficients which best fit the training data, in which the best fit is determined by minimizing a certain cost function. After it has found the coefficients of (β), it makes the predictions by multiplying the coefficients with the input variables for a given asset. The data inputs are but not limited to, asset prices, trading volume, technical indicators, limit order book, order size and time frame.

B2. Algorithm: Artificial Neural Network (ANN)

Artificial neural networks-based decisions are developed for bidding strategies, risk management, analysing patterns and prediction of performance. The ANN has the ability to learn and generate its own knowledge from data inputs such as orderbooks, bid-ask prices, order flows which try to predict the assets price changes by giving a solution. In time-series problems, the ANN is required to build a forecasting model from the historical data set to predict future data points. In short, ANN model is sufficient to gain insight into the directional change of the market by classifying the demand and supply patterns.

Supply Group

B3. Algorithm: Dynamic Time Warping Algorithm

The dynamic time warping (DTW) algorithm is an efficient method to match patterns inside a trading system. DTW is a time-series alignment algorithm for measuring two sequences of vector values by warping the distance until an optimal match between the sequences is found. Opix Algo implements DTW to automate matching and determination of the trading position at the predicted price range.

Experiment | Dataset | Accuracy Rate |

1 | AUD - USD 2019 (Jan - August) | 70% |

2 | EUR - USD 2019 (Jan - August) | 72% |

C. Opix Algo: Structure of Learning and Training Stage

D. Opix Algo: Performance Metrics

The results are reported with different filter values. Opix Algo breaks the results down into different trading performance metrics as follows:

Percentage of Correct Trades to Perfect Foresight = [ Σ Correct Trades / Σ Trades ] x 100

check on market orders before they are sent to the markets. They allow for automatically blocking or cancelling orders as soon as trades occur outside defined price thresholds, surpass a maximum size, or post and excess number of orders automatically.

allow users or providers to adjust execution parameters during an execution, often when market conditions change, or the algorithm behaves in an undesirable or unexpected way. This is important, for example, in instances of particularly low liquidity when market makers could dominate trading volume or stop trading altogether.

involve continued monitoring of intraday market and carrying trades with counterparties when limits are breached. Algorithms identify errors and potential issues, analysing the particular scenarios and improve execution strategies and risk controls.

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