artificial intelligence, quant and technical analysis for

Artificial Intelligence, Quant and Technical Analysis For Online Brokers Web Development

Avenida Ciudad Barcelona, 10828007 | Madrid

(+34) 915 535 [email protected]

https://www.robexia.com© Noesis Analisis Financiero

CONSULTING AND PROJECT DEVELOPMENT

2021

The technical information that can be provided on a security is practically unlimited, so, in order to help as a Knowledge Curator, we explain our categories;

We offer Artificial Intelligence, Portfolio Tools, Quant and Technical Analysis content

in three categories

QUANT CATEGORY

AI QUANT CATEGORY

INTERACTIVE METRICS CATEGORY

This category is based on quantitative analysis (QA), a research-focused mathematical technique and statistical modeling of the behavior of financial assets from a traditional perspective.

This category uses modeling and research based on powerful and disruptive Artificial Intelligence models, especially Machine Learning, to obtain explanatory patterns and predictions of financial markets.AI

This category reflects the tools that, in an interactive way, allows to obtain metrics of personalized portfolios or to carry out specific simulations.

www.robexia.com ® Noesis Análisis Financiero

Q

IM

Quant AIQuant

InteractiveMetrics

Q1. Basic TA Report

Q5. Ichimoku

Q2. Technical Indicators

Q6. Fundamentals

Q3. Risk Metrics

Q4. Trading Signals

AI 1. Trend Predictor

AI 2. NLP Market Sentiment

AI 3. Technical Indicators Index Clusters

AI 4. Deep Reinforcement Factor Investing

IM 1. Warrant Selector

IM 2. Portfolio Risk Metrics

Q Quant

Q1. Basic TA Report

Q5. Ichimoku

Q2. Technical Indicators

Q6. Fundamentals

Q3. Risk Metrics

Q4. Trading Signals

Quant

Support and resistance levels are areas where the probability of a price reversal increases, changing the trend towards a neutral, bullish or bearish one.

SUPPORT AND RESISTANCE LEVELS

PIVOT POINTS are an easy way to find short-term support and resistance areas where the price can react. There are different ways to calculate them, such as the Standard, Camarilla, Woodie, Demark and Fibonacci.

AppleINDEXES | IBEX 35 | APPLE

Apple Inc. is an American companythat designs and produces equipment software and online services.

May5, 2021

Technical Summary:Action:

Bearish pressure with support level at $124,00.Wait until support level is reached.

MARKET. US | INDEX SP 500 | ISIN. US0378331005 | CODE: APPL

Support and resistance levels Analytic

(+7.00%)

(-3.01%)

138.07

124.24

(+13.09%)

(-9.44%)

144.87

119.01

Resistances

Supports

Performance Risk Trend

Support and resistance levels

Market data

1 day 1 quar 1 year

-0,63%

1 month

2,9%0,25% 1.1%

Short term Mid term Long term Risk

-21,27%-6,45%-1,61% HIGH

Very short Short Mid Long

Pivot Points Trading levels Classic

(+7.85%)

(-3.53%)

138.19

123.61

(+13.10%)

(-9.65%)

144.92

138.19

Resistances

Supports

Date Time Open High Low Close Volumen Dif

05/05/2021 Close 129.20 130.45 127.97 128.10 87,222,782

04/05/2021 131.19 131.49 129.70 127.85 91,266,545

0.25%

-4.69%

i i


Q1. BASIC TA REPORT

i Pivot Points

TRADER RESEARCH

Summary Indicators Risk Metrics

Documentation Glossary

Trading Signals Ichimoku

Fundamental IA QuantInteractive Metrics

AppleINDEXES | IBEX 35 | APPLE

Apple Inc. is an American companythat designs and produces equipment software , and online services.

May5, 2021

Technical Summary:Action:

Bearish pressure with support level at $124,00.Wait until support level is reached.

MARKET. US | INDEX SP 500 | ISIN. US0378331005 | CODE: APPL

Support and resistance levels Analytic

(+7.00%)

(-3.01%)

138.07

124.24

(+13.09%)

(-9.44%)

144.87

119.01

Resistances

Supports

Performance Risk Trend

Support and resistance levels

Market data

1 day 1 quar 1 year

-0,63%

1 month

2,9%0,25% 1.1%

Short term Mid term Long term Risk

-21,27%-6,45%-1,61% HIGH

Very short Short Mid Long

Pivot Points Trading levels Classic

(+7.85%)

(-3.53%)

138.19

123.61

(+13.10%)

(-9.65%)

144.92

138.19

Resistances

Supports

Date Time Open High Low Close Volumen Dif

05/05/2021

Close

Close 129.20 130.45 127.97 128.10 87,222,78204/05/2021 131.19 131.49 129.70 127.85 91,266,545

0.25%-4.69%


Technically, an uptrend is a succession of rising price lows and a bearish trend is a succession of decreasing price highs. Price trends depend on the time period ob-served, varying between short, mid and long terms.

The technical look of the price can be seen from a short or long term perspective, depending on the historical prices period used.

The quotes move in a neutral-bullish range in a narrow range with a near top in 130.541 and base in the 122.00 area. Overcoming any of the two levels that delimit this range would warn of the direction of the subsequent trend.

The bearish phase that occurs between february 23rd and march 24th, going back from levels al 144.873 to 116.210, supposes a moderate correction of the previous rise, that moved up from USD 52.744 to USD 144.973.The current bullish phase, however, remains below the maximum resistive enviroments at 144.873. There is no sign of weakness in the upside trend nor overbought or resistance levels (area of 144.873).

Short term comment

Long term comment

PRICE TRENDS

TECHNICAL COMMENTS

QuantQ1. BASIC TA REPORT

QuantQ2. TECHNICAL INDICATORS

Algorithms based on stock prices or volume that allow detecting overbought or oversold states, certain patterns and signals of maintenance or changes in technical trends.

TECHNICAL INDICATORS

TECHNICAL INDICATORS

Moving Averages

Indicator Signal Actual Previous Interpretation

127,85

129,15

132,16

125,15

MA7

MA 21

MA 50

MA 200

Very short term bullish trend.

Short term bearish trend.

Mid term bearish trend.

Long term bullish trend.

127,12

129,35

132,95

124,55

Bollinger Bands


131,15

126,43

129,92

122,11

Bol 95 Upper Band

Bol 95 Upper Band

Bol 99 Upper Band

Bol 99 Upper Band

Very short term neutral trend.

Very short term neutral-bullish trend.

Very short term bullish trend.

Very shrt term neutral.bullish trend.

131,07

126,33

130,42

124,11

Relative Strenth Indicator (RRSI)


131,15

126,43

129,92

122,11

RSI 7

RSI 21

RSI 40

RSI 200

Overbought + Divergence: Price may reverse downside.

No Signal.

No Signal.

No signal.

131,07

126,33

130,42

124,11

Other Indicators


1.10

-2.20

Momentum 7

Montentum 14

MACD Line-S

Fast Stochastic

Short term neutral trend.

Short term bearish trend.

Short term neutral trend.

No signal,

-0.05

-1.85

i i


Quant

AD-Accumulation Distribution ADXAroon ARAroon OscillatorASI – Accumulative SwingATR-Average True RangeBollinge BandsCCI-Commodity ChannelChaikin VolatilityCMF-Chaikin Money FlowCMO-Chande Momentum OscillatorRWI

Dynamic MomentumEase of movementEnvelopesForce IndexFractal Adaptative Moving AverageHeikin AshiHull Moving AveragesKaufman Adaptative MAKeltner ChannelMACDMomentumMoving Averages On Balance VolumeRSIRVIChande Momentum

StochasticSwing IndexTriple Exponential MATRIXTrue Strength IndexUltimate OscillatorVolume Adjusted Moving AverageVertical Horizontal FilterWilliams %RZigzagZero lag exponential Moving Average Parabolic SARUltimate OscillatorDEMA

Detrended Price OscillatorAcceleration Bands (ABANDS)Adaptive Moving AverageAbsolute Price OscillatorPercent Price Oscillator Welles Wilder’s Smoothing AverageTriple Exponential Moving Average T3Price Volume Trend Rate of Change ROC

Since the advent of traditional technical indicators, this field has expanded with remarkable success, creating improved, more flexible and adaptable versions of classic indicators, as well as new, more powerful and agile indicators and guidelines.

Advanced Harmonic Patterns

Stochastic-RSI Indicator

Volatility-Adjusted Stochastic Oscillator

The K's Envelopes

Alternative Bollinger Bands

Moving Average Contrarian Indicator

Volatility-Adjusted Momentum Indicator

Adaptive MA

Augmented Bollinger Bands

Dynamic Relative Strength Index

Democratic Indicator

Stationary Extreme Indicator

TRADITIONAL INDICATORS

NEW WAVE INDICATORS


Q2. TECHNICAL INDICATORS

Technical indicators can also be adapted to generate continuous or discrete buy and sell signals. Usually, indicator systems can be runned in different ways. For all of them, backtesting and metrics are available.

System

Non Stop Reversion

Reversion

Performance

Win to Loss Ratio

Profit to Loss Ratio

mid

54.6%

1.10

Performance


24.60%

1.10

Distribution

10

2019 - 07

2019 - 10

2020 - 01

2020 - 04

2020 - 07

2021 - 01

2021 - 04

20

40

60

80

100Apple Inc.RSI 7 NonStop Rev

TRADING INDICATORS


Quant

System

Non Stop Reversion

Reversion

Performance

Win to Loss Ratio


mid

54.6%

1.10

Performance


24.60%

1.10

Distribution

10

2019 - 07

2019 - 10

2020 - 01

2020 - 04

2020 - 07

2021 - 01

2021 - 04

20

40

60

80

100Apple Inc.RSI 7 NonStop Rev

Q2. TECHNICAL INDICATORS

Risk metrics, focused on volatilities and value at risk (VaR), allow investors to evaluate the type of asset.

Volatilities and Values at Risk (VAR)

Metric Level Actual Previous Interpretation

127,85

129,15

132,16

125,15

Short Term Volatility Annualized

Long Term Volatility Annualized

Short Term Daily VaR

Long Term Daily VaR

30 days anual volatility is 43.85%

250 days anual volatility is 45.17%

Short view: For every equity a daily 0.77% is under risk (95% confidence)

Long view: For every equity a daily 0.78% is under risk (95% confidence)

127,12

129,35

132,95

124,55

RISK METRICS

0,786 0,786 0,786 0,786 0,786

VaR

CP

LP

i i

43,55 43,70 43,85 44,00 44,15

Volatility

CP

LP

i i


QuantQ3. RISK METRICSQ3. RISK METRICSQ3. RISK METRICS

DowSP 500

Nasdaq 100

NONE 10% HIGH 30% VERY HIGH 50% 60% 70% 80% 90% HIGHEST

VaR 95 currentVaR 95 1 week agoVaR 95 1 month agoVaR 95 1 year ago

Trading signals detect, through different types of systems, investment opportunities. These opportunities vary in timeframe and in their level of risk, as well as in the pre-diction technique used.

The candlestick patterns, from Japan, are based on the use of candles whose combination with the previous trend allows the investor to detect turning points and enter trades with a 5 day average duration.

Chartism is a technique based on the search for known patterns or figures, such as the head and shoulders or the double top to detect market turns with an average duration higher than other techniques.

CHARTISM PATTERNS

Default searches saved by the user.

Search by instrument.

General searching criteria where the user can select items such as the type of instrument, the market and the country among others.

Select the date range.

Sepecific TA Techniques and events.

CANDLESTICK PATTERNS

TRADING SIGNALS


QuantQ4. TRADING SIGNALS

The japanese technique known as Ichimoku has established itself as a benchmark in the ever-growing Western world for its ability to show the action price trend status at a glance.

ICHIMOKU


QuantQ5. ICHIMOKU

The Ichimoku technique is expanding in the West for the clarity of its bullish and bearish signals. Currently is the most used one in Asia. Major Ichimoku signals (in and out of the kumo, averages crossing and trend or line changes, like those in the Chikou, will be detected).

ADVANTAGES

Ichimoku signals do not literally correspond to patterns, but have a common structure.

SIMILAR INFORMATION

Detected patternDatePattern descriptionExpected moveLinksEntry, exit and stop loss levelsChart

Our fundamentals product shows the latest financial statements and a general overview of the financial performance and ESG score of a given company. This product is custom-made according to our customers’ needs and requirements and is available for several markets including european and american companies.

FUNDAMENTALS

FUNDAMENTALS

TOTAL (Fr) LAST UPDATE | 31-March-21

Currency

CountryISIN

ExchangeSector

Industry

EmployeesMarket Capitaization

Shares (Millions)

Listed Shares Book Value

€ EUR

FRFR0000120271

EuronextEnergy

Oil & Gas

104.460127.9632.586

2.47344,3

Sales

EBIT

Pre-tax Income

Net IncomeProfit Margin

ROA

ROEBPA

MOST RECENT QUARTER

43,737

4,8635,051

3,412

5,47%

3,91%8,38%

2,90%

31 March 21

GENERAL INFORMATION RESULTS

i i

ESG

COMPARATIVEPERCETILE




ENVIROMENT

SOCIAL

GOVERNANCE

81,3

82,9

85,1

84,0

Alcohol

Animal Testing

Controversial Weapons

Minor Weapons

Leather

GMO

Pesticides

Palm Oil

Coal

Tobacco

TOTAL SCORE OUT OF 100 31 - March - 2021

i i

FUNDAMENTALS


QuantQ6. FUNDAMENTALS

AI AI Quant

AI 1. Trend Predictor

AI 2. NLP Market Sentiment

AI 3. Technical Indicators Index Clusters

AI 4. Deep Reinforcement Factor Investing (coming Soon)

AI QuantAI 1. TREND PREDICTOR

Our Trend Predictor forecasts the probabily that a financial instrument will rise or fall in a given period of time. For example: What is the probability that Amazon will rise or fall by 5% in 2 days, 3 days or 10 days.

Supervised Algorithms: Hidden Markov Model (HMM) and Logistic Regression

TREND PREDICTOR


INDEXES | IBEX

TREND PREDICTOR

INDEXES | IBEX | BANKINTER

TREND PREDICTOR

i Select Time Frame

DAYS

TEF

ACS

REP

INDITEX

BBVA

BANKINTER

-3%

5,12%

3,03%

3,01%

2,01%

3,13%

-2%

10,15%

10,06%

7,02%

12,06%

9,17%

-1%

15,24%

18,12%

17,12%

19,16%

19,22%

0%

23,30%

26,21%

25,20%

26,25%

26,30%

+1%

16,23%

20,15%

20,12%

21,19%

19,22%

+2%

9,18%

14,08%

2,88% 8,15% 18,22% 24,29% 16,24% 9,16%

12,07%

16,13%

12,14%

+3%

4,15%

2,05%

4,20%

2,03%

3,09%

2,11%

3%%

2%

0

-1%

-2%

1%

4,20%

1 day

9,16%

24,29%

18,22%8,15%

16,24%

5,13%

1 week

11,16%

21,29%

16,22%

10,15%

13,24%

8,13%

1 month

16,16%

18,29%

13,22%

13,15%

10,24%

10,13%

3 months

18,16%

16,29%

10,22%

16,15%

7,24%

15,13%6 months

18,16%

14,19%

7,22%19,15%

7,24%

7

6

5

4

3

2

1

1-1-2-3-4-5 2 3 4 5 6 7 8 9 10 11

4,2

1 day

The probability thatBANKINTER rises 3%

is 4.2% in 1 day.

i ii

INDEXES | IBEX | BANKINTER

TREND PREDICTOR

AI QuantAI 1. TREND PREDICTOR

TREND PREDICTOR


7

6

5

4

3

2

1

1 day 1 week 1 month

4,2

1 day

The probability thatBANKINTER rises 3%

is 4.2% in 1 day.

i ii

INDEX | NASDAQ | AMAZON

NLP MARKET SENTIMENT

70% Postive Sentiment

+ 70%

-20%

N 10%

Positive

NegativeNeutral

no data included

Financial Bloggers

100% Positive

Tw

+

50% Positive

NegativeNeutral

+N

-

Tw

67% Positive

Negative

New Agencies

+-

SOURCE + N -

Tw

Tw

3

2

2

0

7

0

1

0

0

1

0

1

1

0

2

News agencies

FinancialBloggers

Total

SENTIMENT SOURCESi i

Our NLP algorithm is connected to different news channels and social networks, such as twitter, and is able, through natural language processing, to process and analyze large amounts of natural language data, focused on classifying news and tweets on a specific topic into positive, neutral and negative sentiment. It also calculates the probability of real impact in the market. It is intended to extract market sentiment from listed companies or Stock Indexes and can be parameterized by the customer and working in real time.

Unsupervised Algorithms: Text Mining, Neural Networks and Tensor Methods.



AI QuantAI 2. NLP MARKET SENTIMENT




0 +100-100Today

70% Positive

yesterday today 1 week ago 1 month ago

News agenciesTw FinancialBloggers TotalTw



0

10

20

30

40

50+100

-100NEGATIVE

POSITIVE

NNEUTRAL

i i

70% Positive


SENTIMENT SOURCES


AI QuantAI 2. NLP MARKET SENTIMENT

Price

INDEX | IBEX

TECHNICAL INDICATORS INDEX CLUSTERS

B-SV

Bullish

Bearish

Neutral

Bu Bu

Bu Be

BeBe

Bu-OB Bu-OB

A-OB

Bu-OB B-OS

B-OS

CLUSTERS

BuBu-OB Be Be-OS

BULLISHBULISH OVER - BOUGHTBEARISHBEARISH OVER - SOLD


Neutral


Bearish

BuBu-OB Be Be-OS

BuBu-OB Be Be-OS

Bullish

i i

SMA RatioATR 5ATR 15ATR RatioADX 5ADX 15SMA Vdome RatioStochastic 5Stochastic 15Stochastic RatioRSI 5RSI 15RSI Ratio

INDICATORSi i

Our model is in charge of determining appropriate groups of stocks depending on the technical situation analyzed with technical indicators and factors. For example: If we choose an index, the EuroStoxx 50, our model clusters these companies by how they behave in returns and with a set of technical indicators, having the stocks for each cluster, where each cluster represents, groups of volatile, overbought, oversold, etc.

Supervised Algorithms: Density-Based Spatial Clustering of Applications with Noise and Gaussian mixture model.

CLUSTERING WITH TECHNICAL INDICATORS


AI QuantAI 3. TECHNICAL INDICATORS INDEX CLUSTERS

INDEX | IBEX

TECHNICAL INDICATORS INDEX CLUSTERS

Bullish

Bearish

Neutral

Bu Bu

Bu Be

BeBe

Bu-OB Bu-OB

A-OB

Bu-OB B-OS

B-OS

Bullish

Bearish

Neutral

Bu Bu

Bu Be

BeBe

Bu-OB Bu-OB

A-OB

Bu-OB B-OS

B-OS

i

iName

Santander

Repsol

BBVA

SP

SP

SP

Banking

Energy

Banking

Bearish

Bearish

Bearish

-

-

-

-

-

-

Country Sector Trend Return Average

NEUTRAL BEARISH OVERBOUGHT

CLUSTERING WITH TECHNICAL INDICATORS


AI QuantAI 3. TECHNICAL INDICATORS INDEX CLUSTERS

Reinforcement Learning is one of the three basic paradigms of machine learning, along with supervised and unsupervised learning.

It is related with intelligent agents and their requirement to take actions in an environment in order to improve the acummulation of “rewards”

Our DRFI deploys millions of simulations per second in which it makes buy-sell decisions on a portfolio and which are evaluated with positive or negativereinforcements, such as return, risk or portfolio stability, allowing the machine to learn directly from the results of the decisions taken and adapting itself to real life trading.

DEEP REINFORCEMENT FACTOR INVESTING

AI QuantAI 4. DEEP REINFORCEMENT FACTOR INVESTING


DEEP REINFORCEMENT FACTOR INVESTING

NEW STATEPORTFOLIOINPUTFEATURES

INPUT

OUTPUT REBALANCEACTION

OUTPUT

7

6

5

4

3

2

1

Performance

REWARD / PUNISHMENTMAX - PROFIT

PUNISH DROWDOWNS

SHARPE RATIO

PUNISH LONG PERIODS

EPSILON

DISCOUNT

LEARNING RATE

LOOKBACK RATE

HYPER PARAMETERS

TRAINING LEARNING

NEURAL NETWORK

VALUE

QUALITY

SIZE

VOLATILITY

BUY

HOLD

SELL

AppleTesla

AXASantander

BBVA Amazon

COMINGSOON

IM1. Warrant Selector

InteractiveMetricsIM

IM2. Portfolio Risk Metrics

WARRANT SELECTOR

Trading with warrants requires special and particular treatment, especially through the use of sensitivity metrics to the evolution of the underlying asset, time or volatility. Warrant Selector allows the simulation of scenarios in a simple and intuitive way for the end investor.

WARRANT SELECTOR

WARRANTS | AMADEUS

WARRANT SELECTOR

WARRANTS | AMADEUS

Warrant

AMS

2,25 €

61,70 €

+ 0,10

-0,68

+44%

-1,09%

Implemented Volatility 46,12

-

CALL WARRANTS AMADEUS | 40.000 | 18-March-2021i i

61,70

62,14

i i SIMULATOR

Simulation

UNDERLYING ASSET PRICE WARRANT PRICE

Current

IMPLIEDVOLATILITYJun Jul Aug Sept Oct Nov

2,25

3,25

Current

Simulation

Call Warrant AMS 40.000 SG ISSUER O6/18/2020

Call Warrant AMS 55.000 SG ISSUER O6/18/2020

Call Warrant AMS 55.000 Societe/Generale O6/18/2020

Call Warrant AMS 62.000 BNP Paribas ARBITRAGE INSURANCE

i i

FIXED DATA

Last quotation ------- 03/15/2021

Expire date ----------- 06/18/2021Warrants Issued ---- 300.000,00

(Call/Put) ----------------------- Call

Ratio ------------------------- 0,1000

Implied Volatility (%) 52,00

Leverrage --------------- 27,40

Intrinsic Value ---------- 21,64

Days Until Expiration ---- 14

Delta (%) -------------- 100,00

Vega ------------------- 0,0000

Thetha (Euros) -------- 0,0011

Gamma --------------- 0,0000

Elasticity (%) -------------- 27,4

DYNAMIC DATA

HIGH RISK

HIGH LOW


Interactive metricsInteractive metricsIM 1. WARRANT SELECTOR

This product allows our customers to get Risk metrics calculations such as VaR and Volatility in real time via web services or api calls, of a given portfolio. This tool allows the final user to get an overview of their entire portfolio.

PORTFOLIO RISK METRICS

PORTFOLIO OVERVIEW

Name

Playtech 4,5

Softbank 5,25

Renault 2,5MSS Global Brands

Renta 4 Pegasus

Number of Tittles800.000

500.000

500.000

1056,92

12936,78

Last Price (€)

103,30

115,18

92,99

102,55

16,20

Value

826.384

575.875

501.340

108386,84

209533,76

Currency

€

€

€

€

€

Type

Fixed Income

Fixed Income

Fixed Income

Funds

Funds

PORTFOLIO OVERVIEW

Name

Playtech 4,5

Softbank 5,25

Renault 2,5MSS Global Brands

Renta 4 Pegasus

Number of Tittles800.000

500.000

500.000

1056,92

12936,78

Last Price (€)

103,30

115,18

92,99

102,55

16,20

Value

826.384

575.875

501.340

108386,84

209533,76

Fixed Income

Funds

25%

75%

PORTFOLIO

Total Stocks Funds Fixed Income

Total Stocks Funds Fixed Income


Interactive metricsInteractive metricsIM 2. PORTFOLIO RISK METRICS

Documentation Levels

LEVEL 1: Glossary.

Users will be able to place the cursor across the terms used in all our products where a pop-up will show with the correspond-ing definition.

Each of our products will have different levels of helpful information to guide the final user through each product. Our clients will be able to choose among 3 different options for all our products.

LEVEL 2: Description.

LEVEL 3: Description video.

Users will be able to place the cursor across the terms used in all our proucts, where a pop-up will show with and extended description of the term plus an explanatory image.

Users will be able to click on the video icon found across the main terms in all our products to play the corresponding description video.

“Implied Volatility”

i

i“Implied Volatility”

i“Implied Volatility”

iDefinition:

Implied volatility is the level of volatili-ty (deviation) that is matched to a specific price of an option or warrant.

iDescription:Volatility is the level of risk or deviation from a series of prices. However, the implicit one is the level of volatility implicit in a specific Price of an option or warrant. In other words, the greater the volatility expected in the market, the more risk of deviation is assumed to the prices and therefore the higher the demanded Price will be.It could be compared to the insurance premium: the greater the risk of the insured event occurring (greater volatility and movement in the price of the underlying), the higher the insurance Premium (higher price of the option or warrant).


Technical Content AccessThere are three waysby default to accessthe content:

We design fully adapted pages regarding look & feel, UX, etc. using the client’s CSS and following the requirements. These pages are embedded with a wrapper on the client's website via <div> or <include> containers via JavaScript or any equivalent method.

‘Frame’ Direct integration

API / Web

The client access our servers with one of the standard services or we design it’s own customized service.

This way, the client receives the required information by programming the queries as internally arranged. It is up to the client to "paint" the information on their systems.

XML / CSV / accessed via Api

The data model is similar to the previous one, but the data objects are "physically" transmitted.

It is less flexible but in certain technologies or for certain uses (populate internal db, etc.) it can be a better suited solution.

Receive your .csv file


All the content in English is also available in other languages (Spanish, German, French, Catala, etc), although they may not be enabled/permissioned.

From Monday to Friday, a daily-updated technical analysis report of listed companies is made. The groups correspond to geographical areas -each one with a different number of shares- that cover approximately 99% of the market volume, avoiding all the numerous instruments that, although listed, lack special relevance and liquidity.

� � ��

1. USA2. United Kingdom3. Canada4. Italy5. France6. Germany7. Spain8. World Indexes9. Argentina

19. Singapore20. Japan21. Colombia22. Chile23. Greece24. India25. Criptocurrencies26. China27. Australia

28. Vietnam29. Brazil30. Mexico31. South Africa32. Taiwan33. Commodities

10. Neederlands11. Denmark12. Iceland13. Finland14. Ireland15. Hong Kong16. Israel17. Forex18. Korea

Online Technical Analysis For Brokers

Languages


Why us?We have been creating algorithms, content, academic research and technical development for 20 years around the world. We combine Technology with algorithms and Technical Analysis Expertise.

We accompany the client in the consulting, development and deployment of projects with a model based on quality, creativity and a deep understanding both in the case of just following requirements and when the adjacement possibilities must become a reality.We work with scenarios in which maintaining maximum simplicity and cost efficiency is our philosophy.

Technologicalskills

Math & statistics

datascience

academicresearch

artificialintelligence

Tecnical analysisexpertise

THIS IS THE FOUNDATION OF OUR EXPERIENCE IN CONSULTING AND PROJECT DEVELOPMENT



Robexia AI TechConsulting is a Noesis AF Artificial Intelligence and

Quantitative Laboratory. Noesis was founded in 1998 and has developed its

activity for more than two decades in the financial sector.

Artificial Intelligence techniques are based on supervised, unsupervised and

reinforcement Machine Learning. Specifically, analysis and

predictions are carried out using variables from the field of probability

theory in conjunction with models and techniques of machine learning and

neural networks.

Some examples are Random Forest, Support Vector Machines, KNN and

Deep Learning through neural networks from the simplest ones to ganns.

Learn about us...


CONSULTING AND PROJECT DEVELOPMENTAutomation of banking processes and Artificial Intelligence


Robexia deploys and maintains both classical and Artificial Intelligence algorithms in the following areas:

*Analysis with a mathematical approach *Optimization*Continuous programming*Integer N-choose-K heuristics

*Chartist patterns detection*Candlestick patterns detection*Ichimoku Kinko Hyo*Heiken Ashi*Elliott Wave*Indicators *Systems

QUANT ANALISIS

QUANTITATIVE RISK MANAGEMENT

TECHNICAL ANALISIS

QUANTITATIVEPORFOLIO MANAGEMENT

*Point vs. probabilistic statements*Inference and learning*Probabilistic Prediction*Discriminant regression*Discriminant classification*Probabilistic graphical models*Applications*Application: probabilistic regression in the stock market*Maximum likelihood*Regularization and features selection*Bayesian*Mixed approach*Bias Reduction*Bias reduction*Functional bias reduction*Linear basis expansion*Quasi-linear adaptive basis*Adaptive networks*Gradient boosting*Kernel trick*Variance Reduction*Background*Shrinkage*Regularization*Ensemble learning*Spatial and dynamic models*Overview*Least squares dynamic models*Wiener-Kolmogorov filtering*Dynamic principal component*Probabilistic linear state space models*Probabilistic state space models

MACHINE LEARNING*Performance definitions*Portfolio P&L*Trading P&L*Implementation shortfall*Returns*Excess performance*Path analysis*Aggregation ex-ante performance*Aggregation*Stock variables*Credit value adjustment*Liquidity value adjustment*Static market/credit risk*Dynamic market/credit risk*Aggregation stress-testing*Stress-testing*Stress-testing in banks*Ex-Ante evaluation: stochastic dominance and risk measures*Ex-ante evaluation*Stochastic dominance*Stochastic dominance fundamentals*Satisfaction/risk measures*Mean-variance trade-off*The fundamental risk quadrangle*Expected utility and certainty-equivalent*Quantile (value at risk)*Expected shortfalland sub-quantile*Enterprise risk management*Spectral satisfaction measures / Distortion expectations*Coherent satisfaction measures*Induced expectations*Non-dimensional ratios*Ex-ante attribution: performance*Bottom-up exposures*Top-down exposures: factors on demand*Relationship between bottom-up and top-down exposures*Joint distribution*Ex-ante attribution: risk*Risk budgeting: general criteria*Homogenous measures and Euler decomposition*Esscher expectation*Diversification management

*Construction: portfolio optimization - Overview*Mean-variance principles*Analytical solutions of the mean-variance problem*Benchmark allocation*Mean-variance pitfalls*Construction Estimation Risk*Estimation risk measurement*Sample-based allocation*Bayesian allocation*Robust allocation*Diversification management*Black-Litterman*Equilibrium prior*Active views*Posterior*Limit cases and generalizations*Signals*Carry signals*Value signals*Technical signals*Microstructure signals*Fundamental and other signals*Signal processing*Construction: cross-sectional strategies*Simplistic portfolio construction*Advanced portfolio construction*Relationship with FLAM and APT*Multiple portfolios*Fundamental law of active management*Construction: time series strategies*The market*Expected utility maximization*Option based portfolio insurance*Rolling horizon convex/concave strategies*Signal induced strategy*Convexity analysis*Execution*High frequency risk drivers*Market impact modeling*Order scheduling*Order placement*S9 Construction - Historical*S9 Construction - Monte Carlo

Avenida Ciudad Barcelona, 10828007 | Madrid

(+34) 915 535 [email protected]

https://www.robexia.com© Noesis Analisis Financiero

[email protected]

Get in touch with our team!

Carlos JuareguizarCEO

Sandra NietoCOO

Camilo IraizozCountry Manager LATAM

Ana MunaizBusiness Development

*Construction: portfolio optimization - Overview*Mean-variance principles*Analytical solutions of the mean-variance problem*Benchmark allocation*Mean-variance pitfalls*Construction Estimation Risk*Estimation risk measurement*Sample-based allocation*Bayesian allocation*Robust allocation*Diversification management*Black-Litterman*Equilibrium prior*Active views*Posterior*Limit cases and generalizations*Signals*Carry signals*Value signals*Technical signals*Microstructure signals*Fundamental and other signals*Signal processing*Construction: cross-sectional strategies*Simplistic portfolio construction*Advanced portfolio construction*Relationship with FLAM and APT*Multiple portfolios*Fundamental law of active management*Construction: time series strategies*The market*Expected utility maximization*Option based portfolio insurance*Rolling horizon convex/concave strategies*Signal induced strategy*Convexity analysis*Execution*High frequency risk drivers*Market impact modeling*Order scheduling*Order placement*S9 Construction - Historical*S9 Construction - Monte Carlo

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