Create a simulation to test 1 month of data in 30 mins

WebServer/AIPython/aipredictor.py

@@ -1,3 +1,4 @@
+from datetime import datetime, timedelta
 import os
 import sys
 import joblib
@@ -13,13 +14,29 @@ def Predict():
     # Get the Symbol from ARGV
     Symbol = sys.argv[1]
 
+    # get the number of days ago to run the simulation for
+    DaysBack = int(sys.argv[2])
+
+    print(f"Days back: {DaysBack}")
+
+    # calculate the time offsets
+    end_date = datetime.now() - timedelta(days=DaysBack)
+    start_date = end_date - timedelta(days=70)
+
+    # convert to string formats
+    start_str = start_date.strftime('%Y-%m-%d')
+    end_str = end_date.strftime('%Y-%m-%d')
+    
+    print(f"Start Date: {start_str}")
+    print(f"End Date: {end_str}")
+
     # Define paths (consistent with your previous script)
     SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
     DATA_DIR = os.path.join(SCRIPT_DIR, "data")
     MODEL_PATH = os.path.join(DATA_DIR, "model.keras")
 
     # Pull 1 month of current data to make prediction against | for volatility 20
-    df = yf.download(Symbol, period="2mo", auto_adjust=True)
+    df = yf.download(Symbol, start=start_str, end=end_str, auto_adjust=True, progress=False)
     if not df.empty:
         # Remove the horizontal ticker column
         df.columns = df.columns.get_level_values(0)

WebServer/AIPython/features.py

@@ -78,10 +78,11 @@ def MakeFeatures(df):
             df.drop(col, axis=1, inplace=True)
 
     # Drop rows with null values
-    df.dropna(inplace=True)
+    feature_columns = [col for col in df.columns if col != 'Target_Close'] # Dont drop any Target_Close as we need this data for predictions
+    df.dropna(subset=feature_columns, inplace=True)
 
     # Replace Infinity with 0 -> This fixes the AI mental breakdown
-    df = df.replace([np.inf, -np.inf], 0)
+    df.replace([np.inf, -np.inf], 0, inplace=True)
 
     # Replace Infinity with 0 -> This fixes the AI mental breakdown
     df['Volume_Chg'] = df['Volume_Chg'].replace([np.inf, -np.inf], 0)

WebServer/Components/Pages/Home.razor

@@ -1,5 +1,6 @@
 @page "/"
 @using Controllers.Payment
+@using Controllers.ProjectTest
 @using Microsoft.Extensions.FileSystemGlobbing.Internal.PathSegments
 @rendermode InteractiveServer
 
@@ -46,6 +47,11 @@
                     <input placeholder="Stock Symbol [NVDA]" @bind="addStockSymbol" />
                     <button @onclick="addStock">Add To Tracked Stocks</button>
                 </div>
+                <br />
+                <div>
+                    <button @onclick="RunTest">Run 1Mo Test</button>
+                    <span>@TestResults</span>
+                </div>
                 <span>@resultError</span>
             </div>
         </div>
@@ -195,4 +201,13 @@
         string dbPrefix = $"[{userName.ToLower()}]:"; 
         dbDriver.Set(dbPrefix + "watched", JsonConvert.SerializeObject(Session.TrackedStocks) );
     }
+
+
+    string TestResults = "";
+    async Task RunTest(){
+        D683_Project_Test tests = new D683_Project_Test(aiModule);
+        float score = await tests.Simulate();
+        TestResults = score.ToString();
+    }
+
 }

WebServer/Controllers/Automation.cs

@@ -17,14 +17,17 @@ namespace Controllers.Automation {
             _paymentProcessor = PaymentProcessor;
         }
 
-        public void GlobalPredictAI() {
+        public void GlobalPredictAI(int DaysBefore = 0, bool testmode = false) {
 
             // Start this process on a background thread so its non-blocking
             Task thread = new Task(() => {
 
                 // Load the userlist
-                List<string>? UserList = JsonConvert.DeserializeObject<List<string>>(_dbDriver.Get("Users"));
+                List<string> VerifiedUserList = new List<string>(){ "TESTMODE" };
-                List<string> VerifiedUserList = UserList != null ? UserList : new List<string>();
+                if (!testmode) {
+                    List<string>? UserList = JsonConvert.DeserializeObject<List<string>>(_dbDriver.Get("Users"));
+                    VerifiedUserList = UserList != null ? UserList : new List<string>();
+                }
 
                 // Process each request at the same time for speed improvement
                 Parallel.ForEach(VerifiedUserList, async (username) => {
@@ -40,7 +43,7 @@ namespace Controllers.Automation {
                     
                         // Predict the trend on a new thread
                         Task thread = new Task(() => {
-                            (string, float)Result = _aiModule.PredictAI(cur.Symbol);
+                            (string, float)Result = _aiModule.PredictAI(cur.Symbol, DaysBefore);
 
                             // If error log it
                             if (!string.IsNullOrEmpty(Result.Item1)){

WebServer/Controllers/ProjectTest.cs

@@ -0,0 +1,111 @@
+using Controllers.PythonInterop;
+using Entities;
+using Microsoft.AspNetCore.Razor.TagHelpers;
+
+namespace Controllers.ProjectTest {
+    
+    public class D683_Project_Test {
+
+        // TESTING Starting Money
+        float Money = 1000;
+
+        // TESTING WATCH STOCK LIST
+        List<Stock> TrackedStocks = new List<Stock>() {
+            new Stock(){ Symbol="NVDA" },
+            new Stock(){ Symbol="INTL" },
+            new Stock(){ Symbol="AAPL" },
+            new Stock(){ Symbol="SHOP" },
+            new Stock(){ Symbol="PANW" },
+            new Stock(){ Symbol="BBBY" },
+            new Stock(){ Symbol="REAL" },
+            new Stock(){ Symbol="W" },
+            new Stock(){ Symbol="ROKU" },
+            new Stock(){ Symbol="FUN" }
+        };
+
+        // TESTING STOCK HISTORY
+        List<PurchasedStock> StockHistory = new List<PurchasedStock>();
+
+        AIModule _aiModule;
+        public D683_Project_Test( AIModule aiModule ) {
+            _aiModule = aiModule;
+        }
+
+        public async Task<float> Simulate() {
+            float StartingMoney = Money;
+            // Run once for each day 30 days straight
+            for (int i=30; i>=0; i--) {
+
+                // Go through each watched stock and find the highest prediction
+                List<Task> threadpool = new List<Task>();
+                foreach(Stock cur in TrackedStocks) {
+                
+                    // Predict the trend on a new thread
+                    Task thread = new Task(async () => {
+                        (string, float)Result = _aiModule.PredictAI(cur.Symbol, i);
+
+                        // If error log it
+                        if (!string.IsNullOrEmpty(Result.Item1)){
+                            Console.WriteLine(Result.Item1);
+                        }
+
+                        // Write the score to the users tracked stocks
+                        cur.Score = Result.Item2;
+                    });
+                    thread.Start();
+                    threadpool.Add(thread);
+                }
+
+                // Wait for all processes to finish
+                await Task.WhenAll( threadpool );
+
+                // Get the highest ranked
+                Stock HighestRanking = new Stock(){ Symbol="NVDA", Score = -400 };  // Just a placeholder incase an empty list comes through there is a fallback
+                foreach(Stock cur in TrackedStocks) {
+                    if (HighestRanking.Score < cur.Score) {
+                        HighestRanking = cur;
+                    }
+                }
+
+                // Sell all stocks
+                float totalSale = 0;
+                foreach(PurchasedStock cur in StockHistory) {
+                    if (cur.Sold == false) {
+                        // Get sell price
+                        float sellPrice = cur.Quantity * _aiModule.GetCurrentPrice( cur.Symbol );
+                        // Add up the total sale
+                        totalSale += sellPrice;
+                        // Mark as sold
+                        cur.Sold = true;
+                    }
+                }
+                Money += totalSale;
+
+                // Dont buy anything on the last run
+                if (i != 0) {
+
+                    // Buy predicted stock
+                    float stockPrice = _aiModule.GetCurrentPrice( HighestRanking.Symbol );
+
+                    // Get max stocks user can purchase [ int cast truncates the decimal ]
+                    int MaxQty = (int)( Money / stockPrice );
+
+                    // Add the stock
+                    StockHistory.Add( new PurchasedStock(){
+                        Symbol = HighestRanking.Symbol.ToUpper(),
+                        PurchasePrice = stockPrice,
+                        Quantity = MaxQty,
+                    } );
+                    Money = Money - ( stockPrice * MaxQty );
+                }
+            }
+
+            // Return a score [Bigger than 0 is money earned] or [Less than 0 is money lost]
+            return Money - StartingMoney;
+        }
+
+
+
+
+    }
+}

WebServer/Controllers/PythonInterop.cs

@@ -27,8 +27,8 @@ namespace Controllers.PythonInterop {
         }
 
         // Return ( Error, Signal )
-        public (string, float) PredictAI(string StockSymbol) {
+        public (string, float) PredictAI(string StockSymbol, int DataEndDaysAgo = 0) {
-            (bool, string) Success = PyProcess.RunPythonProcess(_PyPath, _ExecPath + "/aipredictor.py", returns: true, PyArgs: StockSymbol);
+            (bool, string) Success = PyProcess.RunPythonProcess(_PyPath, _ExecPath + "/aipredictor.py", returns: true, PyArgs: $"{StockSymbol} {DataEndDaysAgo}");
             if (!Success.Item1) {
                 return (Success.Item2, 0);
             } else {