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Since December 2018
Instructor since December 2018
Apprendre les bases du trading et établir une stratégie personnalisée.
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From 119.51 $ /h
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Devenir trader ne se fait pas du jour au lendemain.

A travers mes cours vous apprendrez à analyser un marché, identifier les intervenants et les zones d'interventions. Aussi, nous analyserons ensemble les différents indicateurs techniques afin d'établir une stratégie de trading qui vous correspond ( Carnet d'ordre, market profile, graphiques ...). Enfin, nous mettrons également l'accent sur l'importance de la psychologie dans le trading ( 80% de la réussite se base dessus ) et les mécanismes à adopter lors d'une session.

Si aujourd'hui faire du trading est donné à tout le monde, très peu sont les particuliers qui sont rentables sur le long terme. Cela est souvent dû à un manque de formation, à une psychologie pas assez préparée ou à de trop grosses prises de risques.

A travers mes cours, je souhaite expliquer la réalité de ce métier qui, certes est fabuleux mais demande une discipline militaire.
Location
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At student's location :
  • Around Annemasse, France
  • Around Geneva, Switzerland
About Me
Bonjour à tous, j'ai 29 ans et je suis Day-trader sur les marchés des obligations.

J'ai commencé à m'intéresser au trading pendant la crise du Covid-19 car je recherchais une activité dans laquelle je serais libre de mes mouvements et indépendant.

A travers mon apprentissage j'ai pu constater que la réalité du monde de la finance était bien différente de ce qu'on peut croire à travers les médias ou les réseaux sociaux. En effet, c'est un métier qui certes, apporte une liberté exceptionnelle mais qui demande une discipline militaire.

90% des particuliers perdent leur capital. Souvent la faute à un manque de formation, d'informations et de trop grosses prises de risques sur les marchés.

L'objectif à travers nos échanges sera de vous préparer au mieux à relever ce défi et de déterminer une méthode adaptée à votre profil pour devenir un trader rentable.
Education
Bachelor en commerce + Certification AMF ( autorité des marchés financiers ).
Titulaire de l'AFA + Identifié à la FINMA
Baccalauréat en Marketing ( Lycée des Glières )
Experience / Qualifications
Avant le trading j'ai été chef d'entreprise pendant 7 ans. A travers cette expérience, j'ai appris à gérer les caractères et à tirer le meilleur de mes collaborateurs.
Age
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Advanced
Duration
60 minutes
The class is taught in
French
Availability of a typical week
(GMT -05:00)
New York
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At student's home
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
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Mattia
MsC in Engineering with top marks and research assistant of Econometrics for Italian top University.

Business Expert in Risk Management. Academic Research in Quantitative Finance and Algorithmic Trading.

Common discipline covered: Econometrics (with applications in R, Stata, SPSS, Eviews, Gretl), Statistics, Financial Mathematics, Quantitative Support for Master Degree Thesis (from Regressions to all statistical applications), Risk Management, Mathematics, Computer Science

I help with assignments, exams, presentations, advanced research, dissertations, big programming projects and general skill enhancement. Proficient in all major statistical packages: R, SPSS, Stata, Matlab, EViews, Gretl.

Covering university courses all around Europe: Italy, United Kingdom, France, Germany, Spain, Portugal,...


Technical Skills (application and often implementation from scratch):

1) Econometrics: Multivariate Regression, Discrete variable models (i.e. Logit), Time series models (i.e. AR/MA, ARCH/GARCH), Vector AutoRegressive model (VAR), Cointegration (Engle-Granger, VECM), Long-memory process (Fractional Integration), Regime switching models (Hamilton Filter), Kalman Filter, Unobserved Components ARIMA model, Beveridge-Nelson decomposition (Hansen's approach), Copula methods, Metropolis-Hastings algorithm, Black-Litterman model (Meucci's approach), Hierarchical Risk Parity

2) Quantitative Trading (Mid-High Frequency Trading): Stat Arb & Pairs Trading models, Order Imbalance & Order Replenishment effects on intraday returns, Optimal Setup of Entry-Exit Trading Triggers for Quant Trading Strategies, Stat Arb Bertram Model, Data sampling rules for non equally-spaced data (time vs. volume clock for high freq data), Bid-Ask Bounce Bias & Sahalia Method for Microstructure Noise Estimation & Test, Hayashi-Yoshida Lead-Lag Index, D'Aspremont Method for Mean Rev Portfolios, Market Fragmentation in Financial Markets, High-Low prices & Pivot Points trading rule, Trend Following Strategy, Avellaneda-Stoikov Model for Optimal Trading Execution

3) Risk Management: P&L production & analysis for energy trading, VaR & Profit at Risk for energy trading, Merton approach for Credit VaR with/without credit rating migrations, EVT & Copula-based VaR, Stress Test models, Structured Credit Models for Regulatory Risk-Transfer, Additional Value Adjustments for Balance Sheet, Risk Aggregation, Model Risk, Interpolation Methods for multi-year PD Term Structure, Methods for Semidefinite-Positive Corr Matrix Adjustment

4) Financial Mathematics: Longstaff-Schwartz, HJM model (Glasserman's scheme), Greeks with Finite Difference Method, CPPI Products & Cushion Multiplier Setup

5) Machine Learning: Support Vector Machine, Decision Tree, Principal Component Analysis & Regression, XGBoost, Random Forest
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Similar classes
arrow icon previousarrow icon next
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Mattia
MsC in Engineering with top marks and research assistant of Econometrics for Italian top University.

Business Expert in Risk Management. Academic Research in Quantitative Finance and Algorithmic Trading.

Common discipline covered: Econometrics (with applications in R, Stata, SPSS, Eviews, Gretl), Statistics, Financial Mathematics, Quantitative Support for Master Degree Thesis (from Regressions to all statistical applications), Risk Management, Mathematics, Computer Science

I help with assignments, exams, presentations, advanced research, dissertations, big programming projects and general skill enhancement. Proficient in all major statistical packages: R, SPSS, Stata, Matlab, EViews, Gretl.

Covering university courses all around Europe: Italy, United Kingdom, France, Germany, Spain, Portugal,...


Technical Skills (application and often implementation from scratch):

1) Econometrics: Multivariate Regression, Discrete variable models (i.e. Logit), Time series models (i.e. AR/MA, ARCH/GARCH), Vector AutoRegressive model (VAR), Cointegration (Engle-Granger, VECM), Long-memory process (Fractional Integration), Regime switching models (Hamilton Filter), Kalman Filter, Unobserved Components ARIMA model, Beveridge-Nelson decomposition (Hansen's approach), Copula methods, Metropolis-Hastings algorithm, Black-Litterman model (Meucci's approach), Hierarchical Risk Parity

2) Quantitative Trading (Mid-High Frequency Trading): Stat Arb & Pairs Trading models, Order Imbalance & Order Replenishment effects on intraday returns, Optimal Setup of Entry-Exit Trading Triggers for Quant Trading Strategies, Stat Arb Bertram Model, Data sampling rules for non equally-spaced data (time vs. volume clock for high freq data), Bid-Ask Bounce Bias & Sahalia Method for Microstructure Noise Estimation & Test, Hayashi-Yoshida Lead-Lag Index, D'Aspremont Method for Mean Rev Portfolios, Market Fragmentation in Financial Markets, High-Low prices & Pivot Points trading rule, Trend Following Strategy, Avellaneda-Stoikov Model for Optimal Trading Execution

3) Risk Management: P&L production & analysis for energy trading, VaR & Profit at Risk for energy trading, Merton approach for Credit VaR with/without credit rating migrations, EVT & Copula-based VaR, Stress Test models, Structured Credit Models for Regulatory Risk-Transfer, Additional Value Adjustments for Balance Sheet, Risk Aggregation, Model Risk, Interpolation Methods for multi-year PD Term Structure, Methods for Semidefinite-Positive Corr Matrix Adjustment

4) Financial Mathematics: Longstaff-Schwartz, HJM model (Glasserman's scheme), Greeks with Finite Difference Method, CPPI Products & Cushion Multiplier Setup

5) Machine Learning: Support Vector Machine, Decision Tree, Principal Component Analysis & Regression, XGBoost, Random Forest
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