remove unessesary files

backend/main.py

@@ -4,8 +4,15 @@
 from . import models, schemas, database
 import pandas as pd
 import json
+import os, sys
 
-from utils.backtest import run_backtest
+cwd=os.getcwd()
+sys.path.append(f"{cwd}/backend/utils/")
+sys.path.append(f"{cwd}/scripts/")
+
+print("The os is :: ", os.getcwd())
+
+from backend.utils.backtest import run_backtest
 
 models.Base.metadata.create_all(bind=database.engine)
 
@@ -20,11 +27,11 @@ def get_db():
 
 @app.get('/health')
 def check_health():
-    return "healthy"
+    return "API is healthy"
 
 @app.post('/indicators/', response_model=schemas.Indicator)
 def create_indicator(indicator: schemas.IndicatorCreate, db: Session = Depends(get_db)):
-    db_indicator = models.Indicator(**indicator.dict())
+    db_indicator = models.Indicator(**indicator.model_dump())
     db.add(db_indicator)
     db.commit()
     db.refresh(db_indicator)
@@ -37,7 +44,7 @@ def read_indicators(skip: int = 0, limit: int = 10, db: Session = Depends(get_db
 
 @app.post('/scenes/', response_model=schemas.Scene)
 def create_scene(scene: schemas.SceneCreate, db: Session = Depends(get_db)):
-    db_scene = models.Scene(**scene.dict())
+    db_scene = models.Scene(**scene.model_dump())
     db.add(db_scene)
     db.commit()
     db.refresh(db_scene)
@@ -74,6 +81,11 @@ def perform_backtest(scene_id: int, db: Session = Depends(get_db)):
 
     return backtest_results
 
+@app.get('/run_backtest/', response_model=List[schemas.BacktestResult])
+def run_backtests(scene: schemas.SceneCreate, db: Session = Depends(get_db)):
+    db_scene = models.Scene(**scene.model_dump())
+    return db_scene
+
 @app.get('/backtest_results/', response_model=List[schemas.BacktestResult])
 def read_backtest_results(skip: int = 0, limit: int = 10, db: Session = Depends(get_db)):
     backtest_results = db.query(models.BacktestResult).offset(skip).limit(limit).all()

notebooks/backtesting.ipynb

@@ -571,7 +571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -646,7 +646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -655,7 +655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -676,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -689,7 +689,7 @@
     {
      "data": {
       "text/plain": [
-       "[{'Period': 25,\n",
+       "[{'Period': 20,\n",
        "  'Gross Profit': 866.7200000000012,\n",
        "  'Net Profit': 866.7200000000012,\n",
        "  'Number of Trades': 1,\n",
@@ -699,13 +699,13 @@
        "  'Sharpe Ratio': 1.0}]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "run_backtest({\"indicator_name\": \"SMA\", \"period\": 25}, df)"
+    "run_backtest({\"indicator_name\": \"SMA\", \"period\": 20}, df)"
    ]
   }
  ],