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
@@ -948,45 +949,13 @@ def create_backtest_plot(
     return historical_projections
 
 
-if __name__ == "__main__":
-    analysis, df = analyze_bitcoin_prices("prices.csv")
-    # 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)
+def run_projection(df):
+    projections = create_plots(df, start="2016-07-09", project_days=365 * 4)
     print("\nProjected Prices at Key Points:")
     print(projections.iloc[[29, 89, 179, 364]].round(2))  # 30, 90, 180, 365 days
 
-    # Create multiple backtests for different periods
-    backtests = [
-        # First to second halving
-        {
-            "start_date": "2012-11-28",  # First halving
-            "backtest_date": "2016-07-09",  # Second halving
-            "project_days": 1460,  # 4 years
-        },
-        # Second to third halving
-        {
-            "start_date": "2016-07-09",  # Second halving
-            "backtest_date": "2020-05-11",  # Third halving
-            "project_days": 1460,  # 4 years
-        },
-        # Third halving onwards
-        {
-            "start_date": "2020-05-11",  # Third halving
-            "backtest_date": "2024-04-19",  # Fourth halving (projected)
-            "project_days": 1460,  # 4 years
-        },
-        {
-            "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:
+def run_backtest(params, df):
     print(
         f"\nRunning backtest from {params['start_date']} to {params['backtest_date']}"
     )
@@ -998,9 +967,7 @@ if __name__ == "__main__":
     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
-            )
+        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
@@ -1013,3 +980,49 @@ if __name__ == "__main__":
             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 until fourth halving
+        {
+            "start_date": "2012-11-28",
+            "backtest_date": "2024-04-19",
+            "project_days": 1460,
+        },
+        # First until third halving
+        {
+            "start_date": "2012-11-28",
+            "backtest_date": "2020-05-11",
+            "project_days": 1460,
+        },
+        # Second until fourth halving
+        {
+            "start_date": "2016-07-09",
+            "backtest_date": "2024-04-19",
+            "project_days": 1460,
+        },
+    ]
+
+    # Run all backtests
+    for params in backtests:
+        proc = Process(
+            target=run_backtest,
+            args=(
+                params,
+                df,
+            ),
+        )
+        procs.append(proc)
+        proc.start()
+    for proc in procs:
+        proc.join()

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%"