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…
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How to add feature interactions
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…
time series feature transformations – quantiles
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…
How to run sklearn’s GridSearchCV with Tensorflow keras models.
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…
simple example simulation of delta hedging with python
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…
python and derivatives pricing tutorial – brownian motion
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…
Posted in quant finance coding course
python and derivatives pricing tutorial
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…
Posted in OTC derivatives valuation, quant finance coding course Tagged with: otc derivatives, python, quantitative risk analysis
How to prepare for quantitative finance intervew
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…
Posted in OTC derivatives valuation Tagged with: c++, otc derivatives, python, quantitative risk analysis
PCA simple example (principle component analisys)
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…
Libor in arrears convexity adjustment simple example
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)…