Run projections and backtests in parallel.

model.py

@@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 from scipy.stats import norm
 from scipy.signal import savgol_filter
+from multiprocessing import Process
 
 
 # Utility functions
@@ -948,68 +949,80 @@ def create_backtest_plot(
     return historical_projections
 
 
-if __name__ == "__main__":
+def run_projection(df):
-    analysis, df = analyze_bitcoin_prices("prices.csv")
+    projections = create_plots(df, start="2016-07-09", project_days=365 * 4)
-    # create_plots(df)  # Full history
-    # create_plots(df, start='2022-01-01')  # From 2022 onwards
-    # create_plots(df, start='2023-01-01', end='2023-12-31')  # Just 2023
-    # Create plots with different time ranges and projections
-    projections = create_plots(df, start="2011-01-01", project_days=365 * 4)
     print("\nProjected Prices at Key Points:")
     print(projections.iloc[[29, 89, 179, 364]].round(2))  # 30, 90, 180, 365 days
 
+
+def run_backtest(params, df):
+    print(
+        f"\nRunning backtest from {params['start_date']} to {params['backtest_date']}"
+    )
+    backtest_projections = create_backtest_plot(df, **params)
+
+    # Print some key projection points vs actual prices
+    print("\nBacktest Results - Projected vs Actual Prices:")
+    validation_df = df[df["Date"] > params["backtest_date"]]
+    actual_prices = validation_df.set_index("Date")["Close"]
+
+    for days in [30, 90, 180, 365]:
+        target_date = pd.to_datetime(params["backtest_date"]) + pd.Timedelta(days=days)
+        if (
+            target_date in actual_prices.index
+            and target_date in backtest_projections.index
+        ):
+            projected = backtest_projections.loc[target_date]
+            actual = actual_prices.loc[target_date]
+            print(f"\n{days} days out ({target_date.strftime('%Y-%m-%d')}):")
+            print(f"Actual Price: ${actual:,.2f}")
+            print(f"Projected (Expected): ${projected['Expected_Trend']:,.2f}")
+            print(
+                f"Projected Range: ${projected['Lower_95']:,.2f} - ${projected['Upper_95']:,.2f}"
+            )
+
+
+if __name__ == "__main__":
+    analysis, df = analyze_bitcoin_prices("prices.csv")
+    procs = []
+
+    # Create main projection
+    proc = Process(target=run_projection, args=(df,))
+    proc.start()
+    procs.append(proc)
+
     # Create multiple backtests for different periods
     backtests = [
-        # First to second halving
+        # First until fourth halving
         {
-            "start_date": "2012-11-28",  # First halving
+            "start_date": "2012-11-28",
-            "backtest_date": "2016-07-09",  # Second halving
+            "backtest_date": "2024-04-19",
-            "project_days": 1460,  # 4 years
+            "project_days": 1460,
         },
-        # Second to third halving
+        # First until third halving
         {
-            "start_date": "2016-07-09",  # Second halving
+            "start_date": "2012-11-28",
-            "backtest_date": "2020-05-11",  # Third halving
+            "backtest_date": "2020-05-11",
-            "project_days": 1460,  # 4 years
+            "project_days": 1460,
         },
-        # Third halving onwards
+        # Second until fourth halving
         {
-            "start_date": "2020-05-11",  # Third halving
+            "start_date": "2016-07-09",
-            "backtest_date": "2024-04-19",  # Fourth halving (projected)
+            "backtest_date": "2024-04-19",
-            "project_days": 1460,  # 4 years
+            "project_days": 1460,
-        },
-        {
-            "start_date": "2016-07-09",  # Second halving
-            "backtest_date": "2024-04-19",  # Fourth halving (projected)
-            "project_days": 1460,  # 4 years
         },
     ]
 
     # Run all backtests
     for params in backtests:
-        print(
+        proc = Process(
-            f"\nRunning backtest from {params['start_date']} to {params['backtest_date']}"
+            target=run_backtest,
+            args=(
+                params,
+                df,
+            ),
         )
-        backtest_projections = create_backtest_plot(df, **params)
+        procs.append(proc)
-
+        proc.start()
-        # Print some key projection points vs actual prices
+    for proc in procs:
-        print("\nBacktest Results - Projected vs Actual Prices:")
+        proc.join()
-        validation_df = df[df["Date"] > params["backtest_date"]]
-        actual_prices = validation_df.set_index("Date")["Close"]
-
-        for days in [30, 90, 180, 365]:
-            target_date = pd.to_datetime(params["backtest_date"]) + pd.Timedelta(
-                days=days
-            )
-            if (
-                target_date in actual_prices.index
-                and target_date in backtest_projections.index
-            ):
-                projected = backtest_projections.loc[target_date]
-                actual = actual_prices.loc[target_date]
-                print(f"\n{days} days out ({target_date.strftime('%Y-%m-%d')}):")
-                print(f"Actual Price: ${actual:,.2f}")
-                print(f"Projected (Expected): ${projected['Expected_Trend']:,.2f}")
-                print(
-                    f"Projected Range: ${projected['Lower_95']:,.2f} - ${projected['Upper_95']:,.2f}"
-                )

prices.csv

@@ -1,5 +1,5 @@
 "Date","Price","Open","High","Low","Vol.","Change %"
-"11/15/2024","88,096.2","87,297.2","88,348.6","87,276.2","109.10K","0.92%"
+"11/15/2024","89,481.8","87,297.2","90,670.6","87,118.8","159.36K","2.51%"
 "11/14/2024","87,294.0","90,424.8","91,726.7","86,740.2","153.34K","-3.46%"
 "11/13/2024","90,422.2","87,971.2","93,226.6","86,168.4","257.57K","2.82%"
 "11/12/2024","87,941.3","88,665.0","89,929.6","85,122.2","288.68K","-0.82%"