refactor notebooks and add evaluation for quantile models

notebooks/btcusdt_dataset.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [

src/ml/data.py

@@ -0,0 +1,54 @@
+import os
+
+import pandas as pd
+import wandb
+from pytorch_forecasting.data.timeseries import TimeSeriesDataSet
+
+
+def get_dataset_from_wandb(run, window=None):
+    artifact_name = f"{run.project}/{run.config['data']['dataset']}"
+    artifact = wandb.Api().artifact(artifact_name)
+    base_path = artifact.download()
+
+    name = artifact.metadata['name']
+    in_sample_name = f"in-sample-{window or run.config['data']['sliding_window']}"
+    in_sample_data = pd.read_csv(os.path.join(
+        base_path, name + '-' + in_sample_name + '.csv'))
+    out_of_sample_name = f"out-of-sample-{window or run.config['data']['sliding_window']}"
+    out_of_sample_data = pd.read_csv(os.path.join(
+        base_path, name + '-' + out_of_sample_name + '.csv'))
+
+    return in_sample_data, out_of_sample_data
+
+def get_train_validation_split(config, in_sample_data):
+    validation_part = config['data']['validation']
+    train_data = in_sample_data.iloc[:int(len(in_sample_data) * (1 - validation_part))]
+    val_data = in_sample_data.iloc[len(train_data) - config['past_window']:]
+
+    return train_data, val_data
+
+
+def build_time_series_dataset(config, data):
+    data = data.copy()
+    data['weekday'] = data['weekday'].astype('str')
+    data['hour'] = data['hour'].astype('str')
+
+    time_series_dataset = TimeSeriesDataSet(
+        data,
+        time_idx=config['data']['fields']['time_index'],
+        target=config['data']['fields']['target'],
+        group_ids=config['data']['fields']['group_ids'],
+        min_encoder_length=config['past_window'],
+        max_encoder_length=config['past_window'],
+        min_prediction_length=config['future_window'],
+        max_prediction_length=config['future_window'],
+        static_reals=config['data']['fields']['static_real'],
+        static_categoricals=config['data']['fields']['static_cat'],
+        time_varying_known_reals=config['data']['fields']['dynamic_known_real'],
+        time_varying_known_categoricals=config['data']['fields']['dynamic_known_cat'],
+        time_varying_unknown_reals=config['data']['fields']['dynamic_unknown_real'],
+        time_varying_unknown_categoricals=config['data']['fields']['dynamic_unknown_cat'],
+        randomize_length=False,
+    )
+
+    return time_series_dataset

src/ml/model.py

@@ -0,0 +1,13 @@
+import wandb
+from pytorch_forecasting.models.temporal_fusion_transformer import TemporalFusionTransformer
+
+# TODO: Maybe save all models on cpu
+def load_model_from_wandb(run):
+    model_name = run.config['model']['name']
+    model_path = f"{run.project}/model-{run.id}:best"
+    model_artifact = wandb.Api().artifact(model_path)
+
+    if model_name == 'TemporalFusionTransformer':
+        return TemporalFusionTransformer.load_from_checkpoint(model_artifact.file())
+
+    raise ValueError("Invalid model name")

src/strategy/strategy.py

@@ -34,7 +34,36 @@ class BuyAndHoldStrategy(StrategyBase):
             dtype=np.int32)
 
 
-class ModelReturnsPredictionStrategy(StrategyBase):
+class ModelPredictionsStrategyBase(StrategyBase):
+    """Base class for strategies based on model predictions."""
+    def __init__(self,
+                 predictions,
+                 name: str = None):
+        self.predictions = predictions
+        assert 'time_index' in self.predictions.columns
+        assert 'group_id' in self.predictions.columns
+        assert 'prediction' in self.predictions.columns
+
+        self.name = name
+
+
+    def info(self):
+        return {'strategy_name': self.name or 'Unknown model'}
+
+    def run(self, data):
+        # Adds predictions to data, if prediction is unknown for a given
+        # item it will be nan.
+        merged_data = pd.merge(
+            data, self.predictions, on=['time_index', 'group_id'],
+            how='left')
+        
+        return self.get_positions(merged_data)
+
+    def get_positions(self, data):
+        raise NotImplementedError()
+
+
+class ReturnsPredictionStrategy(ModelPredictionsStrategyBase):
     """Strategy that selects position based on returns predictions."""
 
     def __init__(
@@ -42,22 +71,11 @@ class ModelReturnsPredictionStrategy(StrategyBase):
             predictions,
             threshold=0.001,
             name=None):
-        self.predictions = predictions
-        assert 'time_index' in self.predictions.columns
-        assert 'group_id' in self.predictions.columns
-        assert 'prediction' in self.predictions.columns
-
-        self.name = name or "ML Returns prediction"
+        super().__init__(predictions, name=name)
         self.threshold = threshold
 
-    def info(self) -> Dict[str, Any]:
-        return {'strategy_name': self.name}
-
-    def run(self, data):
-        arr = pd.merge(
-            data, self.predictions, on=['time_index', 'group_id'],
-            how='left')['prediction'].to_numpy()
-
+    def get_positions(self, data):
+        arr = data['prediction']
         positions = []
         for i in range(len(arr)):
             if arr[i] > self.threshold:
@@ -70,3 +88,39 @@ class ModelReturnsPredictionStrategy(StrategyBase):
                 positions.append(positions[-1])
 
         return np.array(positions, dtype=np.int32)
+
+
+class PriceQuantilePredictionStrategy(ModelPredictionsStrategyBase):
+    def __init__(
+        self,
+        predictions,
+        name=None):
+        super().__init__(predictions, name=name)
+
+    def info(self):
+        return {'strategy_name': self.name}
+
+    def get_positions(self, data):
+
+        arr_preds = data['prediction'].to_numpy()
+        arr_close_price = data['close_price'].to_numpy()
+
+        positions = []
+        for i in range(len(arr_preds)):
+            if not np.isnan(arr_preds[i]).any():
+                price = arr_close_price[i]
+                pred_low = arr_preds[i][0]
+                pred_high = arr_preds[i][-1]
+                if (pred_low - price) / price > 0.001:
+                    positions.append(LONG_POSITION)
+                    continue
+                elif (pred_high - price) / price < -0.001:
+                    positions.append(EXIT_POSITION)
+                    continue
+            
+            if not len(positions):
+                positions.append(EXIT_POSITION)
+            else:
+                positions.append(positions[-1])
+
+        return np.array(positions, dtype=np.int32)