Bump scikit-learn from 1.2.2 to 1.3.2

AI-InvestiBot/models.py

@@ -78,10 +78,10 @@ class BaseModel:
         information_keys (List[str]): The information keys that describe what the model uses
     """
 
-    def __init__(self, start_date: str = None,
+    def __init__(self, start_date: Optional[Union[date, str]] = None,
                  end_date: Optional[Union[date, str]] = None,
-                 stock_symbol: Optional[Union[date, str]] = "AAPL",
-                 num_days: int = None,
+                 stock_symbol: str = "AAPL",
+                 num_days: Optional[int] = None,
                  information_keys: List[str]=["Close"]) -> None:
         if num_days is None:
             with open(f'Stocks/{stock_symbol}/dynamic_tuning.json', 'r') as file:
@@ -154,7 +154,6 @@ def train(self, epochs: int=1000,
         stock_symbol = self.stock_symbol
         information_keys = self.information_keys
         num_days = self.num_days
-        print(start_date, end_date)
 
         #_________________ GET Data______________________#
         _, data, self.scaler_data = get_relavant_values(
@@ -240,36 +239,14 @@ def save(self, transfer_learning: bool=False, name: Optional[str]=None) -> None:
         with open(f"Stocks/{self.stock_symbol}/min_max_data.json", "w") as json_file:
             json.dump(self.scaler_data, json_file)
 
-    @staticmethod
-    def plot(data):
-        """Plots any np.array that you give in"""
-        days_train = [i for i in range(data.shape[0])]
-        data = data[:, 0]
-        # Plot the actual and predicted prices
-        plt.figure(figsize=(18, 6))
-
-        predicted_test = plt.plot(days_train, data, label='Predicted Test')
-        plt.title(f'TITLE')
-        plt.xlabel("X")
-        plt.ylabel("Y")
-
-        import matplotlib.ticker as ticker
-        plt.gca().xaxis.set_major_locator(ticker.MaxNLocator(7))
-
-        plt.legend(
-            [predicted_test[0]],#[real_data, actual_test[0], actual_train],
-            ['Data']#['Real Data', 'Actual Test', 'Actual Train']
-        )
-        plt.show()
-
     @staticmethod
     def is_homogeneous(arr) -> bool:
         """Checks if any of the models indicators are missing"""
         return len(set(arr.dtype for arr in arr.flatten())) == 1
 
     def test(self, time_shift: int=0, show_graph: bool=False,
              title: str="Stock Price Prediction", x_label: str='', y_label: str='Price'
-             ) -> None:
+             ) -> Tuple[float, float, float, float, bool]:
         """
         A method for testing purposes. 
 
@@ -670,10 +647,7 @@ def predict(self, info: Optional[np.ndarray] = None) -> np.ndarray:
 
 class PriceModel(BaseModel):
     """
-    This is the base class for all the models. It handles the actual training, saving,
-    loading, predicting, etc. Setting the `information_keys` allows us to describe what
-    the model uses. The information keys themselves are retrieved from a json format
-    that was created by getInfo.py.
+    This is the model to predict the price. It is not very good. Use the other one
 
     Args:
         start_date (str): The start date of the training data
@@ -852,8 +826,6 @@ def update_cached_offline(self) -> None:
         scaled_data[0] = scaled_window
         self.cached = scaled_data
 
-        #self.plot(self.cached[0][0])
-
     def profit(self, pred, prev):
         return pred
 

AI-InvestiBot/test.py

@@ -1,14 +1,10 @@
 from models import *
 
-from datetime import datetime, date
-from dateutil.relativedelta import relativedelta
-import json
-
 from pandas_market_calendars import get_calendar
 import pandas as pd
 import numpy as np
 
-from trading_funcs import create_sequences, get_relavant_values
+from trading_funcs import create_sequences, get_relavant_values, plot
 from typing import List
 
 
@@ -46,7 +42,7 @@ def test_indepth(models: List[BaseModel], hold_stocks=False):
         temp_test, expected = model.process_x_y_total(temp, temp2, model.num_days, 0)
         print(temp_test.shape)
         for t in temp_test:
-            model.plot(t[:, 0])
+            plot(t[:, 0])
         processed_data.append(temp_test)
     percent_made = 1
     bought_at = []

AI-InvestiBot/trading_funcs.py

@@ -26,6 +26,7 @@
 
 import numpy as np
 import pandas as pd
+import matplotlib.pyplot as plt
 
 __all__ = (
     'non_daily',
@@ -42,7 +43,8 @@
     'supertrends',
     'kumo_cloud',
     'is_floats',
-    'calculate_percentage_movement_together'
+    'calculate_percentage_movement_together',
+    'plot'
 )
 
 
@@ -491,3 +493,27 @@ def calculate_percentage_movement_together(list1: Iterable, list2: Iterable) ->
     percentage = (count_same_direction / (total - 1)) * 100
     percentage2 = (count_same_space / (total - 1)) * 100
     return percentage, percentage2
+
+def plot(data):
+    """
+    Plots any np.array that you give in
+    Purely for testing
+    """
+    days_train = [i for i in range(data.shape[0])]
+    data = data[:, 0]
+    # Plot the actual and predicted prices
+    plt.figure(figsize=(18, 6))
+
+    predicted_test = plt.plot(days_train, data, label='Predicted Test')
+    plt.title(f'TITLE')
+    plt.xlabel("X")
+    plt.ylabel("Y")
+
+    import matplotlib.ticker as ticker
+    plt.gca().xaxis.set_major_locator(ticker.MaxNLocator(7))
+
+    plt.legend(
+        [predicted_test[0]],
+        ['Data']
+    )
+    plt.show()

README.md

@@ -76,26 +76,15 @@ P.S: Remember to change the api and secret key in secrets.config.
 The project retrieves and caches information in the following manner:
 
 - The `get_info.py` file processes all data obtained from yfinance.
-- The information is stored as a dictionary in a JSON file.
-- The `information_keys` feature retrieves values from each key in the JSON.
-
-## Unique Indicators in Models
-
-The models in this project incorporate unique indicators as follows:
-
-- Models utilize the `information_keys` attribute.
-- These keys correspond to the names of indicators created from `get_info.py`.
-- The model retrieves a dictionary from the JSON file and extracts the list associated with the key.
-- Features in the form of NumPy arrays are then fed into the Sequential model.
-- Use different Features by inputing a list of information_keys into either `PriceModel` or `PercentageModel`
+- The information is stored as a dictionary in a JSON file, according to indicator.
+- The `information_keys` feature retrieves values from each key(indicator) in the JSON.
 
 ## Stock Bot Functionality
 
 The stock bot operates based on the following principles:
-
-
-- The AI is implemented into the childclasses of `BaseModel`. 
+
 - Base Model: This is the parent class for all other models and has no data of its own unless specified. Holds functionality for bot NOT AI.
+- The AI is implemented into the childclasses of `BaseModel`.
 - Price Model: This is the base child class that uses data scaled btw high and low of company data and outputs the predicted price
 - Percentage Model: This is the base child class that uses data scaled btw high and low of a window of data(the past num days) and outputs the predicted % change in price
 - Training, testing, saving, and loading are handled by separate functions(Ensuring quality code).
@@ -108,9 +97,6 @@ The stock bot operates based on the following principles:
     - Utilizes data from yfinance.
     - Once 280 days of past data are obtained, the oldest day is removed, and a new day is added at the end.
     - In this case, `model.cached_info` is always a pandas DataFrame or None.
-
-## How the Bot Runs
-
 - The bot identifies the most promising stocks.
 - It utilizes your available funds, following the rules set by the `ResourceManager` class.
 - Stocks are held if their performance exceeds a certain threshold (`MAX_HOLD_INDEX`).
@@ -122,7 +108,7 @@ The stock bot operates based on the following principles:
 
 ## Earnings Processing
 
-The project processes earnings in the following manner:
+The project processes earnings in the following manner becuase it is not daily data:
 
 - All earnings are obtained and separated into two lists: dates and the difference between actual and estimated values.
 - During runtime, earnings outside of a specific range are removed.
@@ -142,6 +128,8 @@ This project offers various models to choose from, including:
 - Price Model: This is the base class that uses data scaled btw high and low of company data and outputs the predicted price
 - Percentage Model: This is the base class that uses data scaled btw high and low of the window data and outputs the predicted % change in price
 
+## For Percentage Model(Price model is not accurate)
+
 - Day Trade Model:
   - Directional Test:  97.88732394366197
   - Spatial Test:  95.07042253521126

requirements.txt

@@ -4,7 +4,7 @@ pandas==2.0.1
 pandas_market_calendars==4.1.4
 yfinance==0.2.18
 tensorflow==2.13.0
-scikit-learn==1.2.2
+scikit-learn==1.3.2
 bs4==0.0.1
 boto3==1.26.152
 alpaca_trade_api==3.0.2