Update CronJob to only run once per day. in the morning

.gitignore

@@ -1,12 +1,7 @@
-stocks.parquet
-stocks_preview.csv
 appdata.db
 __pycache__
 obj
 bin
 *.db
-*.keras
-*.pkl
-*.csv
 
 !/WebServer/AIPython/python/bin

README.MD

@@ -7,11 +7,24 @@ Overview of the directories
 └── WebServer        # The ASP.NET Core 9 Webserver
     ├── AIPython     # All the AI files written in python [Copied to the docker container via the C# compiler]
     ├── Components   # HTML pages written in .razor pages
-    ├── Controllers  # Python Interop Controllers and Database Controlelrs
+    ├── Controllers  # Python Interop Controllers and Database Controller
     ├── Properties   # ASP.Net Properties files
     └── wwwroot      # Statically hosted Web Files
 ```
 
+## Prerequisites
+  
+You need to have docker and docker-compose installed
+
+## Hardware Requrement
+
+You need a computer with docker-compose installed. 
+GPU is not needed as it runs via the CPU runtime. 
+
+Minimum Specs:  
+  4 core CPU. 
+  16GB ram. 
+
 ## Installation
 How to host a local copy
 
@@ -27,4 +40,13 @@ docker compose up -d
 ```
 
 ## Usage
-Navigate to [Localhost:5000](http://localhost:5000).
+
+Navigate to [localhost:5000](http://127.0.0.1:5000).
+
+Register an account with a username and password  
+Add as many stock symbols as you would like to the watched stocks  
+Sit back and let the AI day trade for you  
+This will auto trade once per day based on the AI predicted stock movement 5 days in advance  
+-------------------
+If you would like to simulate your watched stocks on the last 30 days of stock history you can run the 'Run 1Mo Evaluation'  
+This will simulate, such as the real algorithm, the stock trade's per day on your watched stocks so you can get a feel for your watched stocks  

WebServer/AIPython/Target_Close_Average.txt

@@ -0,0 +1 @@
+0.198600759951581

WebServer/AIPython/aipredictor.py

@@ -17,8 +17,6 @@ def Predict():
     # 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)
@@ -27,9 +25,6 @@ def Predict():
     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")

WebServer/AIPython/aitrainer.py

@@ -1,8 +1,19 @@
 import pandas as pd
+from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score
 import features
 import joblib
 import os
+
+# Suppress TensorFlow INFO and WARNING logs
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 
+
+# Suppress specialized XLA and autotuning logs
+os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices=false'
+os.environ['TF_CPP_MAX_VLOG_LEVEL'] = '0'
+
+# CPU only
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
 from sklearn.model_selection import train_test_split
 from keras import Sequential, layers, optimizers
 from keras.callbacks import ReduceLROnPlateau
@@ -19,6 +30,9 @@ def TrainAI():
     # Use external featuers to make sure loaded is the same
     dataset = features.MakeFeatures(dataset)
 
+    # Make sure empty columns dont exist
+    dataset = dataset.dropna()
+
     # Create the X, Y vareables
     X, Y, X_Scaler, Y_Scaler = features.Prepare(dataset)
 
@@ -30,7 +44,7 @@ def TrainAI():
     print(dataset.dtypes)
 
     # Split out the test and train
-    train_features, test_features, train_labels, test_labels = train_test_split(X, Y, test_size=0.2)
+    train_features, test_features, train_labels, test_labels = train_test_split(X, Y, test_size=0.2, shuffle=False) # Keep the training and test data in order as its sequential
 
     # Create the DNN
     dnn_model = Sequential([
@@ -45,9 +59,6 @@ def TrainAI():
         layers.Dense(1, activation='linear')            # DNN layer
     ])
 
-    # Allow negative numbers
-    dnn_model.add(layers.LeakyReLU(alpha=0.01))
-
     # Configure the model
     dnn_model.compile(
         optimizer=optimizers.Adam(learning_rate=0.0001, clipvalue=1.0),
@@ -59,7 +70,7 @@ def TrainAI():
     dnn_model.summary()
 
     # Learning rate reducer
-    reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, min_lr=0.0000001)
+    reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.5, patience=5, min_lr=0.0000001)
 
     # Train the model
     Training_Data = dnn_model.fit( 
@@ -68,8 +79,7 @@ def TrainAI():
         batch_size=64,
         epochs=50,                  # Tuned to the point before overfitting
         verbose=1,                  # Show progress
-        validation_split = 0.2,     # Calculate validation results on 20% of the training data.
-        shuffle=True,
+        shuffle=False,              # Time series data
         callbacks=[reduce_lr]       # Reduce the learning_rate every run
     )
 
@@ -78,6 +88,27 @@ def TrainAI():
         test_features, test_labels, verbose=0
     )
 
+    # Perform test on test data split earlier
+    predictions = dnn_model.predict(test_features)
+
+    # Convert to Binary Direction (The "Signal")
+    y_true_binary = (test_labels > 0).astype(int)
+    y_pred_binary = (predictions > 0).astype(int)
+
+    # Calculate the metrics
+    acc = accuracy_score(y_true_binary, y_pred_binary)
+    prec = precision_score(y_true_binary, y_pred_binary)
+    rec = recall_score(y_true_binary, y_pred_binary)
+    f1 = f1_score(y_true_binary, y_pred_binary)
+
+    # Output the meterics; this gets spit out when training from the python file directly. The C# interop does not output these
+    print(f"Test Loss: {test_results[0]:.4f}")
+    print(f"Test MAE: {test_results[1]:.2%}")
+    print(f"Test Accuracy: {acc:.2%}")
+    print(f"Test Precision: {prec:.2%}")
+    print(f"Test Recall: {rec:.2%}")
+    print(f"F1 Score: {f1:.2%}")
+
     # Save the model
     dnn_model.save(os.path.join(DATA_DIR, "model.keras"))
 

WebServer/AIPython/currentprice.py

@@ -1,3 +1,4 @@
+from datetime import datetime, timedelta
 import sys
 import yfinance as yf
 
@@ -6,8 +7,21 @@ def getCurrentPrice():
     # 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])
+
     ticker = yf.Ticker(symbol)
-    data = ticker.history(period="1d", interval="1m")
+    
+    # Calculate the target date
+    target_date = datetime.now() - timedelta(days=DaysBack)
+    start_str = target_date.strftime('%Y-%m-%d')
+    
+    # End date must be the day AFTER the target to get that day's data
+    end_date = target_date + timedelta(days=1)
+    end_str = end_date.strftime('%Y-%m-%d')
+
+    # Fetch data for that specific 24-hour window
+    data = ticker.history(start=start_str, end=end_str)
     current_price = data['Close'].iloc[-1]
 
     # Return to C# via stdout

WebServer/AIPython/data/stocks.preview.csv

@@ -0,0 +1,201 @@
+Date,Close,High,Low,Open,Volume,Ticker
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WebServer/Components/Pages/Home.razor

@@ -40,19 +40,37 @@
         </div>
 
     <!-- Tool Frame -->
+    <div>
         <div class="gridFrame">
             <div>
                 <div><h2>Actions</h2></div>
-                <div>
-                    <input placeholder="Stock Symbol [NVDA]" @bind="addStockSymbol" />
-                    <button @onclick="addStock">Add To Tracked Stocks</button>
+                    <div>
+                        <input placeholder="Stock Symbol [NVDA]" @bind="addStockSymbol" />
+                        <button @onclick="addStock">Add To Watched Stocks</button>
+                    </div>
+                    <br />
+                    <div>
+                        <button @onclick="RunTest">Run 1Mo Evaluation</button>
+                        <br />
+                        <span>@TestResults</span>
+                    </div>
+                    <span>@resultError</span>
                 </div>
-                <br />
+            </div>
+            <!-- Tracked Stocks -->
+            <div style="margin-top: 20px;" class="gridFrame">
                 <div>
-                    <button @onclick="RunTest">Run 1Mo Test</button>
-                    <span>@TestResults</span>
+                    <h2>Watched Stocks</h2>
+                </div>
+                <div style="column-count: 2;">
+                    @foreach (Stock cur in Session.TrackedStocks){
+                        <div class="signalBlock">
+                            <p style="text-align: center; font-weight: bold;">@cur.Symbol</p>
+                            <p>AI Predicted Score: @cur.Score</p>
+                            <button @onclick="() => {removeStock(cur);}">Remove</button>
+                        </div>
+                    }
                 </div>
-                <span>@resultError</span>
             </div>
         </div>
     }
@@ -60,30 +78,19 @@
     <!-- AI Frame -->
     @if (Session != null){
         <div>
-            <!-- Tracked Stocks -->
-            <div class="gridFrame">
-                <div>
-                    <h2>Current Signal</h2>
-                </div>
-                @foreach (Stock cur in Session.TrackedStocks){
-                    <div class="signalBlock">
-                        <p style="text-align: center; font-weight: bold;">@cur.Symbol</p>
-                        <p>AI Predicted Score: @cur.Score</p>
-                        <button @onclick="() => {removeStock(cur);}">Remove</button>
-                    </div>
-                }
-            </div>
             <!-- Trade History -->
             <div class="gridFrame">
                 <div>
-                    <h2>Trade History</h2>
+                    <h2>@HistoryTitle</h2>
                 </div>
                 @foreach (PurchasedStock cur in Session.TradeHistory){
                     <div class="signalBlock">
                         <p style="text-align: center; font-weight: bold;">@cur.Symbol</p>
                         <p>Purchased Quantity: @cur.Quantity</p>
                         <p>Purchased Price: @cur.PurchasePrice</p>
-                        <p>Sell Price: @cur.PurchasePrice</p>
+                        <p>Sell Price: @cur.PurchasePrice</p><br />
+                        <p>Buy Date: @cur.BuyDate.ToString("MM/dd/yy")</p><br />
+                        <p>Sell Date: @cur.BuyDate.ToString("MM/dd/yy")</p>
                     </div>
                 }
             </div>
@@ -100,7 +107,7 @@
 
     loginSession? Session = null;
     string resultError = "";
-    
+    string HistoryTitle = "Auto Trade History";
 
 
 
@@ -205,9 +212,18 @@
 
     string TestResults = "";
     async Task RunTest(){
+
+        // Make sure a session exists
+        if (Session == null){
+            return;
+        }
+
+        // Simulate 1 month
         D683_Project_Test tests = new D683_Project_Test(aiModule);
-        float score = await tests.Simulate();
-        TestResults = score.ToString();
+        (float, List<PurchasedStock>) score = await tests.Simulate(Session.TrackedStocks);
+        TestResults = $"The amount of money the AI did better than the SPY: ${score} or { score.Item1 / 10 }% better";
+        Session.TradeHistory = score.Item2;
+        HistoryTitle = "Simulated History";
     }
 
 }

WebServer/Components/Pages/Home.razor.css

@@ -62,25 +62,36 @@
 
 .signalBlock {
     display: flex;
+    width: min-content;
+    margin-top: 5px;;
 }
 
 .signalBlock > :nth-child(1) {
     border: solid #040 1px;
     border-radius: 15px;
-    padding: 0px 20px;
+    padding: 5px 20px;
     background-color: greenyellow;
+    align-self: center;
+    margin: 0;
+    width: 100px;
 }
 
 .signalBlock > :nth-child(2) {
     padding: 0 30px;
     font-size: 30px;
     align-content: center;
+    margin: 0;
+    text-wrap: nowrap;
 }
 
 .signalBlock > :nth-child(3) {
-    color: white;
-    padding: 0 30px;
-    width: 40px;
+    padding: 10px 30px;
     border: solid #000 1px;
     border-radius: 15px;
+    background-color: #F44;
+    text-align: center;
 }
+
+    .signalBlock > :nth-child(3):hover {
+        background-color: #f00;
+    }

WebServer/Controllers/Automation.cs

@@ -22,6 +22,13 @@ namespace Controllers.Automation {
             // Start this process on a background thread so its non-blocking
             Task thread = new Task(() => {
 
+                // Check if the day is a weekend
+                DateTime chosenDate = DateTime.Now.AddDays(-DaysBefore);
+                if (chosenDate.DayOfWeek == DayOfWeek.Saturday || chosenDate.DayOfWeek == DayOfWeek.Sunday) {
+                    // If its a weekend skip
+                    return;
+                }
+
                 // Load the userlist
                 List<string> VerifiedUserList = new List<string>(){ "TESTMODE" };
                 if (!testmode) {
@@ -121,6 +128,9 @@ namespace Controllers.Automation {
                     
                     // Get sell price
                     float sellPrice = cur.Quantity * _aiModule.GetCurrentPrice( cur.Symbol );
+                    if (sellPrice == 0) {
+                        return -1f;
+                    }
 
                     // Try create payment session
                     (bool, string) createResult = _paymentProcessor.CreatePayment(username);
@@ -157,6 +167,9 @@ namespace Controllers.Automation {
 
             // Get Stock Price
             float stockPrice = _aiModule.GetCurrentPrice( stockSymbol );
+            if (stockPrice == 0) {
+                return -1f;
+            }
 
             // Get max stocks user can purchase [ int cast truncates the decimal ]
             int MaxQty = (int)( Money / stockPrice );

WebServer/Controllers/ProjectTest.cs

@@ -1,6 +1,5 @@
 using Controllers.PythonInterop;
 using Entities;
-using Microsoft.AspNetCore.Razor.TagHelpers;
 
 namespace Controllers.ProjectTest {
     
@@ -9,20 +8,6 @@ namespace Controllers.ProjectTest {
         // 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>();
 
@@ -31,31 +16,34 @@ namespace Controllers.ProjectTest {
             _aiModule = aiModule;
         }
 
-        public async Task<float> Simulate() {
+        public async Task<(float, List<PurchasedStock>)> Simulate(List<Stock> TrackedStocks) {
             float StartingMoney = Money;
             // Run once for each day 30 days straight
             for (int i=30; i>=0; i--) {
 
+                // Check if the day is a weekend
+                DateTime chosenDate = DateTime.Now.AddDays(-i);
+                if (chosenDate.DayOfWeek == DayOfWeek.Saturday || chosenDate.DayOfWeek == DayOfWeek.Sunday) {
+                    // If its a weekend skip
+                    continue;
+                }
+
                 // 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 () => {
+                    Task thread = new Task(() => {
                         (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 );
 
@@ -68,24 +56,28 @@ namespace Controllers.ProjectTest {
                 }
 
                 // 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 );
+                        float sellPrice = cur.Quantity * _aiModule.GetCurrentPrice( cur.Symbol, i );
+                        if (sellPrice == 0) {
+                            // Account for holidays
+                            goto next;
+                        }
                         // Add up the total sale
-                        totalSale += sellPrice;
+                        Money = Money + sellPrice;
                         // Mark as sold
                         cur.Sold = true;
+                        // Set Sell Date
+                        cur.SellDate = DateTime.Now.AddDays(-i);
                     }
                 }
-                Money += totalSale;
 
                 // Dont buy anything on the last run
                 if (i != 0) {
 
                     // Buy predicted stock
-                    float stockPrice = _aiModule.GetCurrentPrice( HighestRanking.Symbol );
+                    float stockPrice = _aiModule.GetCurrentPrice( HighestRanking.Symbol, i );
 
                     // Get max stocks user can purchase [ int cast truncates the decimal ]
                     int MaxQty = (int)( Money / stockPrice );
@@ -95,13 +87,26 @@ namespace Controllers.ProjectTest {
                         Symbol = HighestRanking.Symbol.ToUpper(),
                         PurchasePrice = stockPrice,
                         Quantity = MaxQty,
+                        BuyDate = DateTime.Now.AddDays(-i)
                     } );
                     Money = Money - ( stockPrice * MaxQty );
                 }
+
+                next:;
             }
 
-            // Return a score [Bigger than 0 is money earned] or [Less than 0 is money lost]
-            return Money - StartingMoney;
+            // Get price of SPY starting and end
+            float SPYbegin = _aiModule.GetCurrentPrice( "SPY", 30 );
+            float SPYend = _aiModule.GetCurrentPrice( "SPY" );
+
+            // Get max quantity for the amount of money given
+            int SPYQty = (int)( StartingMoney / SPYbegin );
+
+            // Calculate how much money you would end with if just stayed in the normal S&P500
+            float earned =  StartingMoney / SPYbegin  * SPYend;
+
+            // Return the difference between the AI and the SPY -> if value returned is 10. The AI did better than the SPY by $10
+            return (Money - earned, StockHistory);
         }
 
 

WebServer/Controllers/PythonInterop.cs

@@ -39,8 +39,8 @@ namespace Controllers.PythonInterop {
             }
         }
 
-        public float GetCurrentPrice(string StockSymbol) {
-            (bool, string) Success = PyProcess.RunPythonProcess(_PyPath, _ExecPath + "/currentprice.py", returns: true, PyArgs: StockSymbol);
+        public float GetCurrentPrice(string StockSymbol, int DataEndDaysAgo = 0) {
+            (bool, string) Success = PyProcess.RunPythonProcess(_PyPath, _ExecPath + "/currentprice.py", returns: true, PyArgs: $"{StockSymbol} {DataEndDaysAgo}");
             if (!Success.Item1) {
                 return 0;
             } else {

WebServer/Entities/LoginSession.cs

@@ -1,6 +1,6 @@
 namespace Entities {
     
-    class loginSession {
+    public class loginSession {
         public string UserName { get; set; } = "";
         public float Money { get; set; } = 0;
         public List<Stock> TrackedStocks { get; set; } = new List<Stock>();

WebServer/Entities/PurchasedStock.cs

@@ -1,11 +1,13 @@
 namespace Entities {
     
-    class PurchasedStock {
+    public class PurchasedStock {
         public string Symbol { get; set; } = "";
         public float Quantity { get; set; } = 0;
         public float PurchasePrice { get; set; } = 0;
         public float SellPrice { get; set; } = 0;
-        public bool Sold = false;
+        public bool Sold { get; set; } = false;
+        public DateTime BuyDate { get; set; } = new DateTime();
+        public DateTime SellDate { get; set; } = new DateTime();
     }
 
 }

WebServer/Entities/Stock.cs

@@ -1,6 +1,6 @@
 namespace Entities {
     
-    class Stock {
+    public class Stock {
         public string Symbol { get; set; } = "";
         public float Score { get; set; } = 0;
     }

crontab.txt

@@ -1,2 +1,2 @@
 0 0 1 * *       root /app/WebServer Retrain-AI >> /var/log/cron.log 2>&1
-0 9-16 * * 1-5  root /app/WebServer Perform-AI >> /var/log/cron.log 2>&1
+0 9 * * 1-5  root /app/WebServer Perform-AI >> /var/log/cron.log 2>&1