Merge pull request #1 from brilhana/develop

Refactored main classes, add base runner

.github/workflow/python-publish.yml

@@ -0,0 +1,31 @@
+name: Upload Python Package
+
+on:
+  release:
+    types: [published]
+
+permissions:
+  contents: read
+
+jobs:
+  deploy:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python
+      uses: actions/setup-python@v3
+      with:
+        python-version: '3.x'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install build
+    - name: Build package
+      run: python -m build
+    - name: Publish package
+      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
+      with:
+        user: __token__
+        password: ${{ secrets.PYPI_API_TOKEN }}

.gitignore

@@ -48,7 +48,7 @@ coverage.xml
 *.cover
 *.py,cover
 .hypothesis/
-.pytest_cache/
+.pY_test_cache/
 cover/
 
 # Translations

LICENSE.md

@@ -1,4 +1,4 @@
-Copyright (c) 2021 Alexandre Brilhante
+Copyright (c)
 
 Permission is hereby granted, free of charge, to any person obtaining
 a copy of this software and associated documentation files (the

README.md

@@ -3,4 +3,9 @@
 ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/quantnet)
 ![GitHub](https://img.shields.io/github/license/brilhana/quantnet)
 
-A PyTorch implementation of [QuantNet: Transferring Learning Across Systematic Trading Strategies](https://arxiv.org/abs/2004.03445).
+A PyTorch implementation of [QuantNet: Transferring Learning Across Systematic Trading Strategies](https://arxiv.org/abs/2004.03445).
+
+## Installation
+```bash
+pip install quantnet
+```

quantnet/app.py

@@ -0,0 +1,238 @@
+import random
+import time
+
+import numpy as np
+import pandas as pd
+
+from quantnet.baselines import (
+    BuyAndHold,
+    CrossSectionalMomentum,
+    RiskParity,
+    TimeSeriesMomentum,
+)
+from quantnet.utils import calc_transaction_costs
+
+
+class QuantNet:
+    def __init__(self, seed=999999999):
+        np.random.seed(seed)
+        random.seed(seed)
+
+        self.data_config = {
+            "data_path": ".\\Tasks\\",
+            "region": ["Asia and Pacific", "Europe", "Americas", "MEA"],
+            "Europe": [
+                "Europe_AEX",
+                "Europe_ASE",
+                "Europe_ATX",
+                "Europe_BEL20",
+                "Europe_BUX",
+                "Europe_BVLX",
+                "Europe_CAC",
+                "Europe_CYSMMAPA",
+                "Europe_DAX",
+                "Europe_HEX",
+                "Europe_IBEX",
+                "Europe_ISEQ",
+                "Europe_KFX",
+                "Europe_OBX",
+                "Europe_OMX",
+                "Europe_SMI",
+                "Europe_UKX",
+                "Europe_VILSE",
+                "Europe_WIG20",
+                "Europe_XU100",
+                "Europe_SOFIX",
+                "Europe_SBITOP",
+                "Europe_PX",
+                "Europe_CRO",
+            ],
+            "Asia and Pacific": [
+                "Asia and Pacific_AS51",
+                "Asia and Pacific_FBMKLCI",
+                "Asia and Pacific_HSI",
+                "Asia and Pacific_JCI",
+                "Asia and Pacific_KOSPI",
+                "Asia and Pacific_KSE100",
+                "Asia and Pacific_NIFTY",
+                "Asia and Pacific_NKY",
+                "Asia and Pacific_NZSE50FG",
+                "Asia and Pacific_PCOMP",
+                "Asia and Pacific_STI",
+                "Asia and Pacific_SHSZ300",
+                "Asia and Pacific_TWSE",
+            ],
+            "Americas": [
+                "Americas_IBOV",
+                "Americas_MEXBOL",
+                "Americas_MERVAL",
+                "Americas_SPTSX",
+                "Americas_SPX",
+                "Americas_RTY",
+            ],
+            "MEA": [
+                "MEA_DFMGI",
+                "MEA_DSM",
+                "MEA_EGX30",
+                "MEA_FTN098",
+                "MEA_JOSMGNFF",
+                "MEA_KNSMIDX",
+                "MEA_KWSEPM",
+                "MEA_MOSENEW",
+                "MEA_MSM30",
+                "MEA_NGSE30",
+                "MEA_PASISI",
+                "MEA_SASEIDX",
+                "MEA_SEMDEX",
+                "MEA_TA-35",
+                "MEA_TOP40",
+            ],
+            "additional_data_path": "_all_assets_data.pkl.gz",
+        }
+
+        self.problem_config = {
+            "export_path": "./Results/",
+            "val_period": 0,
+            "holdout_period": 756,
+        }
+
+        self.model_config = {
+            "baseline": "risk_parity",
+            "buy_and_hold": {},
+            "risk_parity": {"window": 252},
+            "ts_mom": {"window": 252},
+            "csec_mom": {"window": 252, "fraction": 0.33},
+        }
+
+        self.export_label = (
+            "val_period_"
+            + str(self.problem_config["val_period"])
+            + "_testperiod_"
+            + str(self.problem_config["holdout_period"])
+            + "_baseline_"
+            + self.model_config["baseline"]
+        )
+
+        self.data_config["export_label"] = self.export_label
+        self.problem_config["export_label"] = self.export_label
+        self.model_config["export_label"] = self.export_label
+        self.model_config["export_path"] = self.problem_config["export_path"]
+
+        self.X_train_tasks, self.X_val_tasks, self.X_test_tasks = self.get_data(
+            self.data_config, self.problem_config, self.model_config
+        )
+
+    def train(self):
+        if self.model_config["baseline"] == "buy_and_hold":
+            trad_strat = BuyAndHold(self.X_train_tasks, self.model_config)
+            add_label = [""] * len(self.data_config["region"])
+
+        elif self.model_config["baseline"] == "risk_parity":
+            trad_strat = RiskParity(self.X_train_tasks, self.model_config)
+            add_label = [""] * len(self.data_config["region"])
+
+        elif self.model_config["baseline"] == "ts_mom":
+            trad_strat = TimeSeriesMomentum(self.X_train_tasks, self.model_config)
+            add_label = [""] * len(self.data_config["region"])
+
+        elif self.model_config["baseline"] == "csec_mom":
+            trad_strat = CrossSectionalMomentum(self.X_train_tasks, self.model_config)
+            add_label = [""] * len(self.data_config["region"])
+
+        to_add_label = {}
+
+        for lab, region in zip(add_label, self.data_config["region"]):
+            to_add_label[region] = lab
+
+        start = time()
+        trad_strat.train()
+
+        print(time() - start)
+
+        self.X_train_signal = trad_strat.predict(self.X_train_tasks)
+        self.X_val_signal = trad_strat.predict(self.X_val_tasks)
+        self.X_test_signal = trad_strat.predict(self.X_test_tasks)
+
+    def predict(self):
+        results = True
+
+        for region in self.data_config["region"]:
+            region_task_paths = [
+                t + "_all_assets_data.pkl.gz" for t in self.data_config[region]
+            ]
+
+            metrics = True
+
+            for tk, tk_path in zip(self.data_config[region], region_task_paths):
+                pred_train = self.X_train_signal[region][tk][:-1, :]
+                pred_val = self.X_val_signal[region][tk][:-1, :]
+                pred_test = self.X_test_signal[region][tk][:-1, :]
+
+                Y_train = self.X_train_tasks[region][tk][1:, :]
+                Y_val = self.X_val_tasks[region][tk][1:, :]
+                Y_test = self.X_test_tasks[region][tk][1:, :]
+
+                df_train_ret = np.multiply(
+                    pred_train, Y_train
+                ) - calc_transaction_costs(pred_train)
+                df_val_ret = np.multiply(pred_val, Y_val) - calc_transaction_costs(
+                    pred_val
+                )
+                df_test_ret = np.multiply(pred_test, Y_test) - calc_transaction_costs(
+                    pred_test
+                )
+
+                df = pd.read_pickle(self.data_config["data_path"] + tk_path)
+                df_train_ret = pd.DataFrame(df_train_ret, columns=df.columns)
+                df_train_metrics = self.compute_performance_metrics(df_train_ret)
+                df_train_metrics["exchange"] = tk
+
+                df_val_ret = pd.DataFrame(df_val_ret, columns=df.columns)
+                df_val_metrics = self.compute_performance_metrics(df_val_ret)
+                df_val_metrics["exchange"] = tk
+
+                df_test_ret = pd.DataFrame(df_test_ret, columns=df.columns)
+                df_test_metrics = self.compute_performance_metrics(df_test_ret)
+                df_test_metrics["exchange"] = tk
+
+                if metrics:
+                    all_df_train_metrics = df_train_metrics.copy()
+                    all_df_val_metrics = df_val_metrics.copy()
+                    all_df_test_metrics = df_test_metrics.copy()
+                    z = False
+                else:
+                    all_df_train_metrics = pd.concat(
+                        [all_df_train_metrics, df_train_metrics], axis=0
+                    )
+                    all_df_val_metrics = pd.concat(
+                        [all_df_val_metrics, df_val_metrics], axis=0
+                    )
+                    all_df_test_metrics = pd.concat(
+                        [all_df_test_metrics, df_test_metrics], axis=0
+                    )
+
+        all_df_train_metrics["region"] = region
+        all_df_train_metrics["set"] = "train"
+        all_df_val_metrics["region"] = region
+        all_df_val_metrics["set"] = "val"
+        all_df_test_metrics["region"] = region
+        all_df_test_metrics["set"] = "test"
+
+        pd.concat(
+            [all_df_train_metrics, all_df_val_metrics, all_df_test_metrics], axis=0
+        ).to_csv(
+            self.problem_config["export_path"]
+            + region
+            + "_"
+            + self.problem_config["export_label"]
+            + self.to_add_label[region]
+            + ".csv"
+        )
+
+        if results:
+            global_df_test_metrics = all_df_test_metrics.copy()
+            results = False
+        else:
+            global_df_test_metrics = pd.concat(
+                [global_df_test_metrics, all_df_test_metrics.copy()], axis=0
+            )