LesXon
It is a financial and fractal communication language generated with data science.
Data science is a multidisciplinary approach to deriving valuable information from an ever-increasing amount of data.
The LesXon language is pure market data.
General Objective
Create the LesXon language to understand and extract knowledge of the price movement behavior of any financial asset.
Specific Objectives
1
Extract raw transaction data with prices to serve as an input source, using available download tools.
2
Transform raw data for cleaning using data science tools.
3
Process transactions to understand price movement through data analytics.
4
Process transactions to extract knowledge of price behavior through data analytics.
5
Identify the patterns of price structures to create the LesXon language alphabet using data science tools.
6
Detect patterns to use them in the creation of trading strategies.
The LesXon language
* LesXon aims to be the language of the financial markets.
* LesXon language is not a trading strategy.
* LesXon is a language for understanding and extracting knowledge of the price movement behavior of any financial asset.
* LesXon is a data science generated language
* The main idea is that the financial market communicates through the LesXon language, and this is the reason why it is a generated language, i.e. each market communicates in a different way but using the same language.
* The language is part of technical analysis and is inspired by Japanese candlesticks.
* The language is suitable for any temporality and any financial asset because it is fractal.
The LesXon language alphabet
The alphabet of the LesXon language is made up of four letters:
L
T
X
I
Each letter represents a Japanese candlestick from technical analysis.
The language was created with only four letters to mitigate:
1. Data Entropy.
2. The stochastic approach to the uncertainty of price movement.
Advantages of LesXon language
1
Create manual trading strategies.
2
Create algorithmic, automatic or high-frequency trading strategies.
3
Simulate the operation of a strategy, the quality depends on the quality of the transaction data and the quality of the source.
4
Strategies can be backtested.
5
Calculate the mathematical expectation of the strategy.
6
Calculate the probability of price structure patterns.
7
The LesXon language guarantees data quality, as long as ETL processes are performed on the raw data.
8
Customized reports of market information can be created according to the selected financial asset.
9
Make predictions using LesXon language data.
10
Apply data science using LesXon language data.
11
Apply data science to the analytics of a financial asset.
12
Apply data science to the analytics of a pair of financial assets.
Disadvantages of the LesXon language
1
If there is no data, the language cannot be generated.
2
The raw data has a standard format for data loading.
3
If the raw data does not comply with the standard format, ETL processes are required before uploading the data.
4
Knowledge of technical analysis is required.
5
Data science expertise is required to perform data analytics.
The current status of the LesXon Language
1
The ETL process of the raw transaction data has already been successfully performed, which guarantees the quality of the data to take full advantage of the LesXon language.
2
The data science processes for language generation are 100% complete.
3
The data science processes to UNDERSTAND the price movement is 100% done, keep in mind the stochastic approach to the uncertainty of the price movement, this is not eliminated at any time and is a probability management.
4
The data science processes to EXTRACT knowledge of price behavior is 100% done, keep in mind the stochastic approach to the uncertainty of price movement, this is not eliminated at any time and is a probability management.
5
Los procesos de ciencia de datos para la identificación de los patrones de las estructuras del precio para crear el alfabeto del lenguaje LesXon esta realizado al 100%
Startup metrics.
1
If there is no data, the language cannot be generated.
2
The raw data has a standard format for data loading.
3
The approximate transaction amounts of a financial asset processed are as follows:
4
More than 50,000 transactions in one minute.
5
More than 90,000 transactions in 3 minutes.
6
More than 140,000 transactions in 5 minutes.
7
More than 330,000 transactions in 15 minutes.
8
More than 590,000 transactions in 30 minutes.
9
More than 620,000 transactions in 45 minutes.
10
More than 850,000 transactions in 1 hour.
11
More than 1,500,000 transactions in 2 hours.
12
More than 1,900,000 transactions in 3 hours.
13
More than 2,600,000 transactions in 4 hours.
14
More than 8,800,000 transactions in 1 day.
15
More than 46,000,000 transactions in 1 week.
16
More than 180,000,000 transactions in 1 month.
17
It has been downloaded and processed for more than 5 years.
18
The downloaded data is compressed in flat files with a .csv extension.
19
Some files cannot be opened in Excel because of their size or are loaded incomplete.
20
The development time of the project in all its phases from the acquisition of knowledge, implementation and launch into production, from 2018 to 20-08-2023, approximately more than 5 years.
LesXon language in production
1
With the LesXon language, a manual trading strategy of the Scalping type with positive mathematical expectation was created.
2
It was launched into production for real-world testing on Quantfury's trading platform.
3
It was possible to increase an account from 50 USD to 250 USD.
4
A profitability of 400% was achieved.
5
The results were obtained after 2172 operations.
6
The QUANTFURY platform detected the Scalping strategy and applied restrictions.
7
Due to the restrictions, QUANTFURY ceased operations.