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FreqAI enables live deployment as well as backtesting analyses. In both cases, a model is trained periodically, as shown in the following figure. -![freqai-window](assets/freqai_moving-window.png) +![freqai-window](assets/freqai_moving-window.jpg) ### Running the model live @@ -648,7 +648,7 @@ $$ W_i = \exp(\frac{-i}{\alpha*n}) $$ where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. Below is a figure showing the effect of different weight factors on the data points (candles) in a feature set. -![weight-factor](assets/freqai_weight-factor.png) +![weight-factor](assets/freqai_weight-factor.jpg) `train_test_split()` has a parameters called `shuffle` that allows the user to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. @@ -691,7 +691,7 @@ The user can tweak the DI through the `DI_threshold` to increase or decrease the Below is a figure that describes the DI for a 3D data set. -![DI](assets/freqai_DI.png) +![DI](assets/freqai_DI.jpg) #### Removing outliers using a Support Vector Machine (SVM) @@ -728,7 +728,7 @@ DBSCAN is an unsupervised machine learning algorithm that clusters data without Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters the data set by setting all data points that have $N-1$ other data points within a distance of $\varepsilon$ as *core points*. A data point that is within a distance of $\varepsilon$ from a *core point* but that does not have $N-1$ other data points within a distance of $\varepsilon$ from itself is considered an *edge point*. A cluster is then the collection of *core points* and *edge points*. Data points that have no other data points at a distance $<\varepsilon$ are considered outliers. The figure below shows a cluster with $N = 3$. -![dbscan](assets/freqai_dbscan.png) +![dbscan](assets/freqai_dbscan.jpg) FreqAI uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](#https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html)) with `min_samples` ($N$) taken as double the no. of user-defined features, and `eps` ($\varepsilon$) taken as the longest distance in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set.