In many time series machine learning problems the with large number of features the raw data might contain – abnormal / extreme points – discontinuities – stale data To help with determining quickly abnormal or extreme points we can use…
In many time series machine learning problems the with large number of features the raw data might contain – abnormal / extreme points – discontinuities – stale data To help with determining quickly abnormal or extreme points we can use…
Sometimes it is preferable to use simple machine learning algorithms such as logistic regression due to speed and explainability. But usually these simple algorithms do not incorporate interactions of the features (in contrary to , say, neural networks, where sum/difference…
For machine learning algorithms to work well, it’s usually useful to remove noise from features.For time-series this can be achieved in several ways, such as moving averages, applying sign transform, or applying low pass filter. Other, more simple way is…
To find optimal parameters for Neural network one would usually use RandomizedSearchCV or GridSearchCV from sklearn library. Tensorflow keras models, such as KerasClassifier, when calling fit() function does not permit to have different number of neurons. GridSearchCV and RandomizedSearchCV call…
Here we will present simple python code of delta hedging example of a call option . it’s a minimal example with zero interest rates , no dividends. On day 1 we sell 10 near ATM call options and start delta…
In pricing models (black-scholes, local vol, libor market model) the source of uncertainty is Brownian motion. how to simulate it in python? python download first, lets simulate dWt , we know it’s a gaussian variable with mean 0 and deviation…
Tutorial objective: write and understand simple minimal programs in python for pricing financial derivatives topics: Brownian motion objective: draw and calculate properties of brownian motion using python Black scholes pricing objective: calculate call option price Heston model objective: draw forward…
If you apply for quant analyst/quant developer job at investment bank/ hedge fund your quantitative finance interview will generally consist of 4 parts: Programming (C++,python,data structures) General probability/calculus questions Stochastic calculus Derivatives pricing questions for asset class (equity derivatives,interest rate…
simple examples of what PCA does with 2D set of points: ellipsoid like set of points: upper graph is original set of points red vectors are PCA components scaled by explained variance buttom graph is transformed set of points so…
simplified Libor in arrears payoff: pay at time 1 1-year Libor reset at time 1 F(1) $$ \frac{NPV(0)}{P_1(0)}=\mathbb{E}^1 (\frac{F(1)}{P_1(1)}) $$ where $$\mathbb{E}^1$$ is measure with numeraire $$P_1(t)$$ change measure from time 0 to time 1 (time while F(t) is changing)…