Opix Algo​

Risks control  Quality execution Deep analysis

Opix Algo is an algorithm trading service that makes trading decisions by analyzing market microstructure, such as order book dynamics to identify arbitrage trading opportunities in the FX market.

OPIXALGO

ARCHITECTURE

LP 1

LP 2

LP 3

LP 4

Price Feed

Maker Warehouse Volume

Pending Order

Liquidation

Economic Statistic

OPIX TRADE

METADATA
SUMMARY DATA
RAW DATA

Transaction Cost Analysis (TCA)

ENTRY STRATEGY

Evaluate patterns and predict trends

Risk & Error Management

Determine Exit Price Range

Exit Strategy

OPIX ALGO

Value Area

1.0060-1.0070

1.0050-1.0060

1.0040-1.0030

1.0020-1.0030

1.0010-1.0020

Probability Of Profit

80%

50%

20%

60%

90%

DATA INPUT

EXTRACTION
TRANSFORMING
LOADING

ANALYSING MAKER WAREHOUSE VOLUME

VARIABLES AND STRATEGY ANALYSING

ANALYSING MAKER WAREHOUSE VOLUME

ANALYSING MAKER WAREHOUSE VOLUME

Disclaimer

The financial markets changes rapidly and we reserve the right to change our strategies in the best interest of our clients without due notice.

Order Execution
Provide Order Flow Data

LIQUIDITY
PROVIDERS

Order Matching

Opix Algo

Structure Diagram

Speed<1s

Order Filled 100%

Order Submission

Order Routing

Order Execution
Provide Order
Flow Data

LIQUIDITY
PROVIDERS

Order Matching

Opix Algo

Structure Diagram

Speed<1s

Order Filled 100%

Order Submission

Order Routing

Opix Algo

Structure Diagram

Speed< 1s

Order Filled 100%

CEO

No one can accurately predict the markets 100% of the time. It is all about statistics, probability and risk reward ratio.

OpixTrade the backbone of Opix Algo

OpixTrade is a cutting-edge trading and analytical platform that is specifically designed for order flow analysis. It is equipped with all of the tools you need for a deep and convenient market 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:

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.

Linear regression is a classical mathematical and statistical tool used to measure the association between two variables. If there is an independent variable X and a dependent variable Y that depends on X, linear regression can help us to get a linear model that best fits the data set using the equation Y=α+βX.

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.

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.

Date

AUD - USD 2019

EUR - USD 2019

Number of Dataset

Training

Testing

1,390

600

1,340

500

Result

MAE

RSME

0.005

0.0054

0.0048

0.005

Table 1: Result of ANN Model for classifying “Demand” and “Supply” patterns

Date

AUD - USD 2019

EUR - USD 2019

Demand &
Supply Group

Supply Zone

Demand Zone

Supply Zone

Demand Zone

Result

MAE

RSME

0.0454

0.1939

0.081

0.2501

0.013

0.0614

0.0642

0.2283

Table 2: Result of ANN Model for classifying different types of patterns in each group

Table 1: Result of ANN Model for classifying “Demand” and “Supply” patterns

Table 2: Result of ANN Model for classifying different types of patterns in each group

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.

ExperimentDatasetAccuracy Rate
1AUD - USD 2019 (Jan - August)70%
2EUR - USD 2019 (Jan - August)72%

Table 3: DTW algorithm Prediction

Implement patterns of demand and supply

ANN to learn and test the group patterns​

ANN to learn and test different types of patterns in each demand and supply groups

Evaluate the unknown patterns with our ANN model for classification

Implement DTW algorithm to predict the groups

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

Total gain ​

Total Gain = Σ Profit / Loss ​

Largest loss per trade

Largest Loss per Trade = Minimum [ profit/loss ]

Largest gain per trade

Largest Gain per Trade = Maximum [ profit/loss ] ​

Average profit per trade

Average Profit per Trade = Total gain / Total No. of Trades​

Winning trades

Winning Trades = Σ Trades|(profit / loss > 0) ​

Percentage of Winning trades

Percentage of Winning Trades = [ Σ Winning Trades / Σ Buy_Signals + Σ Sell_ Signals ] x 100

Percentage of Correct Trades to Perfect Foresight

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

The analysis method that we stated in this page is the strategy of Opix Algo. It is derived from volume, orders and trades data from our data providers. Our algorithm has shown that linear regression model can determine the variables of microstructure and market depth; the ANN model can classify the patterns of demand and supply zone, and store the values of the resultant trend vector for DTW algorithm to predict the future price range. By the same measure, the makers warehouse volume is an integral piece of data due to the maker’s role and capital in fx markets.

When combine these models, Opix Algo is profitable even with a basic trading strategy. Thus, we conclude Opix Algo is sufficient to gain insight into short-term directional change and ability to trade in high volume price range in the forex market.

PAST PERFORMANCE

Grow your funds with validated algorithms and ensure sustainable wealth generation

RISK

ALGORITHM SAFET

Market risk arises from adverse market moves, potential failures of algorithms, IT systems and processes as well as human errors.

Opix Algo Pre-trade controls

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.

Opix Algo In-flight controls

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.

Opix Algo Post-trade controls

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.