Made a lot of changes to DataCache indicator data is not being saved to the database.

src/BrighterTrades.py

@@ -14,11 +14,14 @@ from trade import Trades
 
 class BrighterTrades:
     def __init__(self):
-        # Object that interacts and maintains exchange_interface and account data
-        self.exchanges = ExchangeInterface()
-
         # Object that interacts with the persistent data.
-        self.data = DataCache(self.exchanges)
+        self.data = DataCache()
+
+        # Object that interacts and maintains exchange_interface and account data
+        self.exchanges = ExchangeInterface(self.data)
+
+        # Set the exchange for datacache to use
+        self.data.set_exchange(self.exchanges)
 
         # Configuration for the app
         self.config = Configuration()
@@ -34,7 +37,7 @@ class BrighterTrades:
                                config=self.config)
 
         # Object that interacts with and maintains data from available indicators
-        self.indicators = Indicators(self.candles, self.users)
+        self.indicators = Indicators(self.candles, self.users, self.data)
 
         # Object that maintains the trades data
         self.trades = Trades(self.users)
@@ -186,8 +189,8 @@ class BrighterTrades:
             :return: bool - True on success.
         """
         active_exchanges = self.users.get_exchanges(user_name, category='active_exchanges')
-        success = False
 
+        success = False
         for exchange in active_exchanges:
             keys = self.users.get_api_keys(user_name, exchange)
             result = self.connect_or_config_exchange(user_name=user_name,
@@ -391,7 +394,8 @@ class BrighterTrades:
         }
 
         try:
-            if self.exchanges.exchange_data.query("user == @user_name and name == @exchange_name").empty:
+            if self.data.get_cache_item().get_cache('exchange_data').query([('user', user_name),
+                                                           ('name', exchange_name)]).empty:
                 # Exchange is not connected, try to connect
                 success = self.exchanges.connect_exchange(exchange_name=exchange_name, user_name=user_name,
                                                           api_keys=api_keys)

src/DataCache_v2.py

@@ -180,7 +180,7 @@ class DataCache:
                 params.append(additional_filter[1])
 
             # Execute the SQL query to remove the row from the database
-            self.db.execute_sql(sql, tuple(params))
+            self.db.execute_sql(sql, params)
             logger.info(
                 f"Row removed from database: table={table}, filter={filter_vals},"
                 f" additional_filter={additional_filter}")

src/Database.py

@@ -85,7 +85,7 @@ class Database:
     def __init__(self, db_file: str = None):
         self.db_file = db_file
 
-    def execute_sql(self, sql: str, params: tuple = ()) -> None:
+    def execute_sql(self, sql: str, params: list = None) -> None:
         """
         Executes a raw SQL statement with optional parameters.
 
@@ -115,22 +115,28 @@ class Database:
                 error = f"Couldn't fetch item {item_name} from {table_name} where {filter_vals[0]} = {filter_vals[1]}"
                 raise ValueError(error)
 
-    def get_rows_where(self, table: str, filter_vals: Tuple[str, Any]) -> pd.DataFrame | None:
+    def get_rows_where(self, table: str, filter_vals: List[Tuple[str, Any]]) -> pd.DataFrame | None:
         """
         Returns a DataFrame containing all rows of a table that meet the filter criteria.
 
         :param table: Name of the table.
-        :param filter_vals: Tuple of column name and value to filter by.
+        :param filter_vals: List of tuples containing column names and values to filter by.
         :return: DataFrame of the query result or None if empty or column does not exist.
         """
         try:
             with SQLite(self.db_file) as con:
-                qry = f"SELECT * FROM {table} WHERE {filter_vals[0]} = ?"
+                # Construct the WHERE clause with multiple conditions
-                result = pd.read_sql(qry, con, params=(filter_vals[1],))
+                where_clause = " AND ".join([f"{col} = ?" for col, _ in filter_vals])
+                params = [val for _, val in filter_vals]
+
+                # Prepare and execute the query with the constructed WHERE clause
+                qry = f"SELECT * FROM {table} WHERE {where_clause}"
+                result = pd.read_sql(qry, con, params=params)
+
                 return result if not result.empty else None
         except (sqlite3.OperationalError, pd.errors.DatabaseError) as e:
             # Log the error or handle it appropriately
-            print(f"Error querying table '{table}' for column '{filter_vals[0]}': {e}")
+            print(f"Error querying table '{table}' with filters {filter_vals}: {e}")
             return None
 
     def insert_dataframe(self, df: pd.DataFrame, table: str) -> int:

src/ExchangeInterface.py

@@ -1,8 +1,10 @@
 import logging
-from typing import List, Any, Dict
+from typing import List, Any, Dict, TYPE_CHECKING
 import pandas as pd
 import ccxt
 from Exchange import Exchange
+from DataCache_v3 import DataCache
+
 
 logger = logging.getLogger(__name__)
 
@@ -17,23 +19,38 @@ class ExchangeInterface:
     Connects, maintains, and routes data requests to/from multiple exchanges.
     """
 
-    def __init__(self):
+    def __init__(self, cache_manager: DataCache):
-        self.exchange_data = pd.DataFrame(columns=['user', 'name', 'reference', 'balances'])
+        self.cache_manager = cache_manager
+        self.cache_manager.create_cache(
+            name='exchange_data',
+            cache_type='table',
+            size_limit=100,
+            eviction_policy='deny',
+            columns=['user', 'name', 'reference', 'balances']
+        )
+
         self.available_exchanges = self.get_ccxt_exchanges()
 
-        # Create a default user and exchange for unsigned requests
+        self.default_ex_name = 'binance'
-        default_ex_name = 'binance'
+        self.default_exchange = None
-        self.connect_exchange(exchange_name=default_ex_name, user_name='default')
-        self.default_exchange = self.get_exchange(ename=default_ex_name, uname='default')
 
-    def get_ccxt_exchanges(self) -> List[str]:
+    def connect_default_exchange(self):
+        if self.default_exchange is not None:
+            return
+        # Create a default user and exchange for unsigned requests
+        self.connect_exchange(exchange_name=self.default_ex_name, user_name='default')
+        self.default_exchange = self.get_exchange(ename=self.default_ex_name, uname='default')
+
+    @staticmethod
+    def get_ccxt_exchanges() -> List[str]:
         """Retrieve the list of available exchanges from CCXT."""
         return ccxt.exchanges
 
-    def get_public_exchanges(self) -> List[str]:
+    @staticmethod
+    def get_public_exchanges() -> List[str]:
         """Return a list of public exchanges available from CCXT."""
         public_list = []
-        file_path = 'src\working_public_exchanges.txt'
+        file_path = r"src\working_public_exchanges.txt"
 
         try:
             with open(file_path, 'r') as file:
@@ -70,8 +87,12 @@ class ExchangeInterface:
         :param exchange: The Exchange object to add.
         """
         try:
-            row = {'user': user_name, 'name': exchange.name, 'reference': exchange, 'balances': exchange.balances}
+            row = pd.DataFrame([{
-            self.exchange_data = add_row(self.exchange_data, row)
+                'user': user_name, 'name': exchange.name,
+                'reference': exchange, 'balances': exchange.balances}])
+
+            cache = self.cache_manager.get_cache('exchange_data')
+            cache.add_table(df=row)
         except Exception as e:
             logger.error(f"Couldn't create an instance of the exchange! {str(e)}")
             raise
@@ -87,7 +108,9 @@ class ExchangeInterface:
         if not ename or not uname:
             raise ValueError('Missing argument!')
 
-        exchange_data = self.exchange_data.query("name == @ename and user == @uname")
+        cache = self.cache_manager.get_cache('exchange_data')
+        exchange_data = cache.query([('name', ename), ('user', uname)])
+
         if exchange_data.empty:
             raise ValueError('No matching exchange found.')
 
@@ -100,7 +123,9 @@ class ExchangeInterface:
         :param user_name: The name of the user.
         :return: A list of connected exchange names.
         """
-        return self.exchange_data.loc[self.exchange_data['user'] == user_name, 'name'].tolist()
+        cache = self.cache_manager.get_cache('exchange_data')
+        exchanges = cache.query([('user', user_name)])
+        return exchanges['name'].tolist()
 
     def get_available_exchanges(self) -> List[str]:
         """Get a list of available exchanges."""
@@ -114,9 +139,10 @@ class ExchangeInterface:
         :param name: The name of the exchange.
         :return: A Series containing the balances.
         """
-        filtered_data = self.exchange_data.query("user == @user_name and name == @name")
+        cache = self.cache_manager.get_cache('exchange_data')
-        if not filtered_data.empty:
+        exchange = cache.query([('user', user_name), ('name', name)])
-            return filtered_data.iloc[0]['balances']
+        if not exchange.empty:
+            return exchange.iloc[0]['balances']
         else:
             return pd.Series(dtype='object')  # Return an empty Series if no match is found
 
@@ -127,12 +153,15 @@ class ExchangeInterface:
         :param user_name: The name of the user.
         :return: A dictionary containing the balances of all connected exchanges.
         """
-        filtered_data = self.exchange_data.loc[self.exchange_data['user'] == user_name, ['name', 'balances']]
+        # Query exchange data for the given user
-        if filtered_data.empty:
+        cache = self.cache_manager.get_cache('exchange_data')
-            return {}
+        exchanges = cache.query([('user', user_name)])
 
-        balances_dict = {row['name']: row['balances'] for _, row in filtered_data.iterrows()}
+        # Select 'name' and 'balances' columns for all rows
-        return balances_dict
+        filtered_data = exchanges.loc[:, ['name', 'balances']]
+
+        # Return a dictionary where exchange 'name' is the key and 'balances' is the value
+        return {row['name']: row['balances'] for _, row in filtered_data.iterrows()}
 
     def get_all_activated(self, user_name: str, fetch_type: str = 'trades') -> Dict[str, List[Dict[str, Any]]]:
         """
@@ -142,16 +171,24 @@ class ExchangeInterface:
         :param fetch_type: The type of data to fetch ('trades' or 'orders').
         :return: A dictionary indexed by exchange name with lists of active trades or open orders.
         """
-        filtered_data = self.exchange_data.loc[self.exchange_data['user'] == user_name, ['name', 'reference']]
+        cache = self.cache_manager.get_cache('exchange_data')
+        exchanges = cache.query([('user', user_name)])
+
+        # Select the 'name' and 'reference' columns
+        filtered_data = exchanges.loc[:, ['name', 'reference']]
+
         if filtered_data.empty:
             return {}
 
         data_dict = {}
+
+        # Iterate over the filtered data
         for name, reference in filtered_data.itertuples(index=False):
             if pd.isna(reference):
                 continue
 
             try:
+                # Fetch active trades or open orders based on the fetch_type
                 if fetch_type == 'trades':
                     data = reference.get_active_trades()
                 elif fetch_type == 'orders':
@@ -222,6 +259,7 @@ class ExchangeInterface:
         :return: The current price.
         """
         if price_source is None:
+            self.connect_default_exchange()
             return self.default_exchange.get_price(symbol=symbol)
         else:
             raise ValueError(f'No implementation for price source: {price_source}')

src/Users.py

@@ -2,9 +2,9 @@ import copy
 import datetime as dt
 import json
 import random
-from typing import Any
 from passlib.hash import bcrypt
 import pandas as pd
+from typing import Any
 from DataCache_v3 import DataCache
 
 
@@ -21,6 +21,16 @@ class BaseUser:
         :param data_cache: Object responsible for managing cached data and database interaction.
         """
         self.data = data_cache
+        # Create a table-based cache with specified columns
+        self.data.create_cache(name='users',
+                               cache_type='table',
+                               size_limit=100,
+                               eviction_policy='deny',
+                               default_expiration=dt.timedelta(hours=24),
+                               columns=["id", "user_name", "status", "chart_views", "email",
+                                        "active_exchanges", "configured_exchanges", "password",
+                                        "api_keys", "signin_time", "active_indicators"]
+                               )
 
     def get_id(self, user_name: str) -> int:
         """
@@ -29,7 +39,7 @@ class BaseUser:
         :param user_name: The name of the user.
         :return: The ID of the user as an integer.
         """
-        return self.data.fetch_item(
+        return self.data.fetch_datacache_item(
             item_name='id',
             cache_name='users',
             filter_vals=('user_name', user_name)
@@ -42,7 +52,7 @@ class BaseUser:
         :param id: The id of the user.
         :return: The name of the user as a str.
         """
-        return self.data.fetch_item(
+        return self.data.fetch_datacache_item(
             item_name='user_name',
             cache_name='users',
             filter_vals=('id', id)
@@ -55,10 +65,9 @@ class BaseUser:
         :param user_name: The name of the user to remove from the cache.
         """
         # Remove the user from the cache only
-        self.data.remove_row(
+        self.data.remove_row_from_datacache(cache_name='users',
-            cache_name='users',
+                                            filter_vals=[('user_name', user_name)],
-            filter_vals=('user_name', user_name), remove_from_db=False
+                                            remove_from_db=False)
-        )
 
     def delete_user(self, user_name: str) -> None:
         """
@@ -66,10 +75,8 @@ class BaseUser:
 
         :param user_name: The name of the user to delete.
         """
-        self.data.remove_row(
+        self.data.remove_row_from_datacache(filter_vals=[('user_name', user_name)],
-            filter_vals=('user_name', user_name),
+                                            cache_name='users')
-            cache_name='users'
-        )
 
     def get_user_data(self, user_name: str) -> pd.DataFrame | None:
         """
@@ -81,10 +88,8 @@ class BaseUser:
         :raises ValueError: If the user is not found in both the cache and the database.
         """
         # Attempt to fetch the user data from the cache or database via DataCache
-        user = self.data.get_or_fetch_rows(
+        user = self.data.get_rows_from_datacache(
-            cache_name='users',
+            cache_name='users', filter_vals=[('user_name', user_name)])
-            filter_vals=('user_name', user_name)
-        )
 
         if user is None or user.empty:
             raise ValueError(f"User '{user_name}' not found in database or cache!")
@@ -100,9 +105,9 @@ class BaseUser:
         :param new_data: The new data to be set.
         """
         # Use DataCache to modify the user's data
-        self.data.modify_item(
+        self.data.modify_datacache_item(
             cache_name='users',
-            filter_vals=('user_name', username),
+            filter_vals=[('user_name', username)],
             field_name=field_name,
             new_data=new_data
         )
@@ -154,7 +159,7 @@ class UserAccountManagement(BaseUser):
         :return: True if the password is correct, False otherwise.
         """
         # Retrieve the hashed password using DataCache
-        user_data = self.data.get_or_fetch_rows(cache_name='users', filter_vals=('user_name', username))
+        user_data = self.data.get_rows_from_datacache(cache_name='users', filter_vals=[('user_name', username)])
 
         if user_data is None or user_data.empty:
             return False
@@ -238,13 +243,13 @@ class UserAccountManagement(BaseUser):
     def user_attr_is_taken(self, attr: str, val: str) -> bool:
         """
         Checks if a specific user attribute (e.g., username, email) is already taken.
-
         :param attr: The attribute to check (e.g., 'user_name', 'email').
         :param val: The value of the attribute to check.
         :return: True if the attribute is already taken, False otherwise.
         """
         # Use DataCache to check if the attribute is taken
-        return self.data.is_attr_taken(cache_name='users', attr=attr, val=val)
+        user_cache = self.data.get_rows_from_datacache('users', [(attr, val)])
+        return True if not user_cache.empty else False
 
     def create_unique_guest_name(self) -> str | None:
         """
@@ -262,7 +267,7 @@ class UserAccountManagement(BaseUser):
             username = f'guest_{suffix}'
 
             # Check if the username already exists in the database
-            if not self.data.get_or_fetch_rows(cache_name='users', filter_vals=('user_name', username)):
+            if not self.data.get_rows_from_datacache(cache_name='users', filter_vals=[('user_name', username)]):
                 return username
 
             attempts += 1
@@ -298,7 +303,7 @@ class UserAccountManagement(BaseUser):
             raise ValueError("Attributes must be a tuple of single key-value pair dictionaries.")
 
         # Retrieve the default user template from the database using DataCache
-        default_user = self.data.get_or_fetch_rows(cache_name='users', filter_vals=('user_name', 'guest'))
+        default_user = self.data.get_rows_from_datacache(cache_name='users', filter_vals=[('user_name', 'guest')])
 
         if default_user is None or default_user.empty:
             raise ValueError("Default user template not found in the database.")
@@ -314,8 +319,10 @@ class UserAccountManagement(BaseUser):
         # Remove the 'id' column before inserting into the database
         new_user = new_user.drop(columns='id')
 
-        # Insert the modified user data into the database, skipping cache insertion
+        # Insert the modified user as a single row, skipping cache
-        self.data.insert_df(df=new_user, cache_name="users", skip_cache=True)
+        columns = tuple(new_user.columns)
+        values = tuple(new_user.iloc[0])
+        self.data.insert_row_into_datacache(cache_name="users", columns=columns, values=values, skip_cache=True)
 
     def create_new_user(self, username: str, email: str, password: str) -> bool:
         """
@@ -464,7 +471,7 @@ class UserIndicatorManagement(UserExchangeManagement):
         user_id = int(self.get_id(user_name))
 
         # Fetch the indicators from the database using DataCache
-        df = self.data.get_or_fetch_rows(cache_name='indicators', filter_vals=('creator', user_id))
+        df = self.data.get_rows_from_datacache(cache_name='indicators', filter_vals=[('creator', user_id)])
 
         # If indicators are found, process the JSON fields
         if df is not None and not df.empty:
@@ -492,25 +499,11 @@ class UserIndicatorManagement(UserExchangeManagement):
                 columns = ('creator', 'name', 'visible', 'kind', 'source', 'properties')
 
                 # Insert the row into the database and cache using DataCache
-                self.data.insert_row(cache_name='indicators', columns=columns, values=values)
+                self.data.insert_row_into_datacache(cache_name='indicators', columns=columns, values=values)
 
             except Exception as e:
                 print(f"Error saving indicator {indicator['name']} for creator {indicator['creator']}: {str(e)}")
 
-    def remove_indicator(self, indicator_name: str, user_name: str) -> None:
-        """
-        Removes a specific indicator from the database and cache.
-
-        :param indicator_name: The name of the indicator to remove.
-        :param user_name: The name of the user who created the indicator.
-        """
-        user_id = int(self.get_id(user_name))
-        self.data.remove_row(
-            filter_vals=('name', indicator_name),
-            additional_filter=('creator', user_id),
-            cache_name='indicators'
-        )
-
     def get_chart_view(self, user_name: str, prop: str | None = None):
         """
         Fetches the chart view or one specific property of it for a specific user.

src/indicators.py

@@ -1,10 +1,10 @@
 import json
 import random
 from typing import Any, Optional, Dict
-
 import numpy as np
 import pandas as pd
 import talib
+import datetime as dt
 
 # A dictionary to hold both indicator types and their corresponding classes.
 indicators_registry = {}
@@ -255,16 +255,35 @@ indicators_registry['MACD'] = MACD
 
 
 class Indicators:
-    def __init__(self, candles, users):
+    def __init__(self, candles, users, cache_manager):
         # Object manages and serves price and candle data.
         self.candles = candles
 
         # A connection to an object that handles user data.
         self.users = users
 
-        # Collection of instantiated indicators objects
+        # A connection to an object that handles all data.
-        self.indicators = pd.DataFrame(columns=['creator', 'name', 'visible',
+        self.cache_manager = cache_manager
-                                                'kind', 'source', 'properties', 'ref'])
+
+        # Cache for storing instantiated indicator objects
+        cache_manager.create_cache(
+                                    name='indicators',
+                                    cache_type='table',
+                                    size_limit=100,
+                                    eviction_policy='deny',
+                                    default_expiration=dt.timedelta(days=1),
+                                    columns=['creator', 'name', 'visible', 'kind', 'source', 'properties', 'ref']
+                                  )
+
+        # Cache for storing calculated indicator data
+        cache_manager.create_cache('indicator_data', cache_type='row', size_limit=100,
+                                   default_expiration=dt.timedelta(days=7), eviction_policy='evict')
+
+        # Cache for storing display properties indicators
+        cache_manager.create_cache('user_display_properties', cache_type='row',
+                                   size_limit=100,
+                                   default_expiration=dt.timedelta(days=1),
+                                   eviction_policy='evict')
 
         # Available indicator types and classes from a global indicators_registry.
         self.indicator_registry = indicators_registry
@@ -341,27 +360,34 @@ class Indicators:
         :return: dict - A dictionary of indicator names as keys and their attributes as values.
         """
         user_id = self.users.get_id(username)
+
         if not user_id:
             raise ValueError(f"Invalid user_name: {username}")
 
+        # Fetch indicators based on visibility status
         if only_enabled:
-            indicators_df = self.indicators.query("creator == @user_id and visible == 1")
+            indicators_df = self.cache_manager.get_rows_from_datacache('indicators', [('creator', user_id), ('visible', 1)])
         else:
-            indicators_df = self.indicators.query('creator == @user_id')
+            indicators_df = self.cache_manager.get_rows_from_datacache('indicators', [('creator', user_id)])
 
-            if indicators_df.empty:
+        # Check if the DataFrame is empty
-                # Attempt to load from storage.
+        if indicators_df is None or indicators_df.empty:
-                self.load_indicators(user_name=username)
+            return {}  # Return an empty dictionary if no indicators are found
-                indicators_df = self.indicators.query('creator == @user_id')
 
-        # Create the dictionary
         result = {}
+
+        # Iterate over the rows and construct the result dictionary
         for _, row in indicators_df.iterrows():
-            # Include all properties from the properties dictionary, not just a limited subset.
+            # Ensure that row['properties'] is a dictionary
+            properties = row.get('properties', {})
+            if not isinstance(properties, dict):
+                properties = {}
+
+            # Construct the result dictionary for each indicator
             result[row['name']] = {
                 'type': row['kind'],
                 'visible': row['visible'],
-                **row['properties']  # This will include all properties in the dictionary
+                **properties  # Merge in all properties from the properties field
             }
 
         return result
@@ -374,21 +400,21 @@ class Indicators:
             :param indicator_names: List of indicator names to set as visible.
             :return: None
         """
+        indicators = self.cache_manager.get_rows_from_datacache('indicators', [('creator', user_id)])
         # Validate inputs
-        if user_id not in self.indicators['creator'].unique():
+        if indicators.empty:
-            # raise ValueError(f"Invalid user_name: {user_name}")
-            # Nothing may be loaded.
             return
-        # Set visibility for all indicators of the user
-        self.indicators.loc[self.indicators['creator'] == user_id, 'visible'] = 0
 
-        # Set visibility for the specified indicator names
+        # Set visibility for all indicators off
-        self.indicators.loc[self.indicators['name'].isin(indicator_names), 'visible'] = 1
+        self.cache_manager.modify_datacache_item('indicators', [('creator', user_id)], field_name='visible', new_data=0)
+
+        # Set visibility for the specified indicators on
+        self.cache_manager.modify_datacache_item('indicators', [('creator', user_id), ('name', indicator_names)],
+                                                 field_name='visible', new_data=1)
 
     def edit_indicator(self, user_name: str, params: dict):
         """
         Edits an existing indicator's properties.
-
         :param user_name: The name of the user.
         :param params: The updated properties of the indicator.
         """
@@ -398,33 +424,15 @@ class Indicators:
 
         # Get the indicator from the user's indicator list
         user_id = self.users.get_id(user_name)
-        indicator_row = self.indicators.query('name == @indicator_name and creator == @user_id')
+        indicator = self.cache_manager.get_rows_from_datacache('indicators', [('name', indicator_name), ('creator', user_id)])
 
-        if indicator_row.empty:
+        if indicator.empty:
             raise ValueError(f"Indicator '{indicator_name}' not found for user '{user_name}'.")
 
-        # Update the top-level fields
+        # Modify indicator.
-        top_level_keys = ['name', 'visible', 'kind']  # Top-level keys, expand this if needed
+        self.cache_manager.modify_datacache_item('indicators',
-        for key, value in params.items():
+                                                 [('creator', params.get('user_name')), ('name', params.get('name'))],
-            if key in top_level_keys and key in indicator_row.columns:
+                                                 field_name=params.get('setting'), new_data=params.get('value'))
-                self.indicators.at[indicator_row.index[0], key] = value
-
-        # Update 'source' dictionary fields
-        if 'source' in indicator_row.columns and isinstance(indicator_row['source'].iloc[0], dict):
-            source_dict = indicator_row['source'].iloc[0]  # Direct reference, no need for reassignment later
-            for key, value in params.items():
-                if key in source_dict:
-                    source_dict[key] = value
-
-        # Update 'properties' dictionary fields
-        if 'properties' in indicator_row.columns and isinstance(indicator_row['properties'].iloc[0], dict):
-            properties_dict = indicator_row['properties'].iloc[0]  # No copy, modify directly
-            for key, value in params.items():
-                if key in properties_dict:
-                    properties_dict[key] = value
-
-        # Save the updated indicator for the user in the database.
-        self.users.save_indicators(indicator_row)
 
     def new_indicator(self, user_name: str, params) -> None:
         """
@@ -457,86 +465,93 @@ class Indicators:
 
         # Create indicator.
         self.create_indicator(creator=user_name, name=indcr, kind=indtyp, source=source, properties=properties)
-        # Update the watch-list in config.
-        self.save_indicator(self.indicators.loc[self.indicators.name == indcr])
 
     def process_indicator(self, indicator, num_results: int = 1) -> pd.DataFrame | None:
         """
-            Trigger execution of the indicator's analysis against an updated source.
+        Trigger execution of the indicator's analysis against an updated source.
 
-            :param indicator: A named tuple containing indicator data.
+        :param indicator: A named tuple or dict containing indicator data.
-            :param num_results: The number of results being requested.
+        :param num_results: The number of results being requested.
-            :return: The results of the indicator analysis as a DataFrame.
+        :return: The results of the indicator analysis as a DataFrame.
         """
         username = self.users.get_username(indicator.creator)
         src = indicator.source
         symbol, timeframe, exchange_name = src['symbol'], src['timeframe'], src['exchange_name']
 
+        # Retrieve necessary details to instantiate the indicator
+        name = indicator.name
+        kind = indicator.kind
+        properties = json.loads(indicator.properties)
+
         # Adjust num_results to account for the lookup period if specified in the indicator properties.
-        if 'period' in indicator.ref.properties:
+        if 'period' in properties:
-            num_results += indicator.ref.properties['period']
+            num_results += properties['period']
 
         # Request the data from the defined source.
         data = self.candles.get_last_n_candles(num_candles=num_results,
                                                asset=symbol, timeframe=timeframe,
                                                exchange=exchange_name, user_name=username)
 
-        # Calculate the indicator using the retrieved data.
+        # Instantiate the indicator object based on the kind
-        return indicator.ref.calculate(candles=data, user_name=username, num_results=num_results)
+        indicator_class = self.indicator_registry[kind]
+        indicator_obj = indicator_class(name=name, indicator_type=kind, properties=properties)
+
+        # Run the calculate method of the indicator
+        return indicator_obj.calculate(candles=data, user_name=username, num_results=num_results)
 
     def get_indicator_data(self, user_name: str, source: dict = None,
                            visible_only: bool = True, start_ts: float = None,
                            num_results: int = 1000) -> Optional[Dict[str, Any]]:
         """
-            Loop through enabled indicators in a user's watch-list. Run the appropriate
+        Loop through enabled indicators in a user's watch-list. Run the appropriate
-            update function and return a dictionary containing all the results.
+        update function and return a dictionary containing all the results.
 
-            :param user_name: The name of the user making the request.
+        :param user_name: The name of the user making the request.
-            :param source: Pass in a source definition to return only results against a particular source.
+        :param source: Pass in a source definition to return only results against a particular source.
-            :param visible_only: Returns only results marked visible.
+        :param visible_only: Returns only results marked visible.
-            :param start_ts: The timestamp to begin the analysis at. (Not implemented yet)
+        :param start_ts: The timestamp to begin the analysis at. (Not implemented yet)
-            :param num_results: The number of results requested.
+        :param num_results: The number of results requested.
-            :return: A dictionary of timestamped data returned from each indicator indexed by the indicator's name,
+        :return: A dictionary of timestamped data returned from each indicator indexed by the indicator's name,
-                     or None if no indicators matched the query.
+                 or None if no indicators matched the query.
         """
         if start_ts:
-            print("Warning: start_ts has not implemented in get_indicator_data()!")
+            print("Warning: start_ts has not been implemented in get_indicator_data()!")
 
         user_id = self.users.get_id(user_name=user_name)
 
-        # Construct the query based on user_name and visibility.
+        visible = 1 if visible_only else 0
-        query = f"creator == {user_id}"
-        if visible_only:
-            query += " and visible == 1"
 
         # Filter the indicators based on the query.
-        indicators = self.indicators.loc[
+        indicators = self.cache_manager.get_rows_from_datacache('indicators', [('creator', user_id), ('visible', visible)])
-            (self.indicators['creator'] == user_id) & (self.indicators['visible'] == 1)]
 
         # Return None if no indicators matched the query.
         if indicators.empty:
-            # Attempt to re-load from db
+            return None
-            self.load_indicators(user_name=user_name)
-            # query again.
-            indicators = self.indicators.loc[
-                (self.indicators['creator'] == user_id) & (self.indicators['visible'] == 1)]
-            if indicators.empty:
-                return None
 
         if source:
-            # Filter indicators by these source parameters.
+            # Convert 'source' column to dictionaries if they are strings
-            if 'market' in source:
+            indicators['source'] = indicators['source'].apply(lambda x: json.loads(x) if isinstance(x, str) else x)
-                symbol = source['market']['market']
+
-                timeframe = source['market']['timeframe']
+            # Extract relevant fields from the source's market
-                exchange = source['market']['exchange']
+            source_timeframe = source.get('market', {}).get('timeframe')
-                indicators = indicators[indicators.source.apply(lambda x: x['symbol'] == symbol and
+            source_exchange = source.get('market', {}).get('exchange')
-                                                                          x['timeframe'] == timeframe and
+            source_symbol = source.get('market', {}).get('market')
-                                                                          x['exchange_name'] == exchange)]
+
-            else:
+            # Extract fields from indicators['source'] and compare directly
-                raise ValueError(f'No implementation for source: {source}')
+            mask = (indicators['source'].apply(lambda s: s.get('timeframe')) == source_timeframe) & \
+                   (indicators['source'].apply(lambda s: s.get('exchange_name')) == source_exchange) & \
+                   (indicators['source'].apply(lambda s: s.get('symbol')) == source_symbol)
+
+            # Filter the DataFrame using the mask
+            filtered_indicators = indicators[mask]
+
+        # If no indicators match the filtered source, return None.
+        if indicators.empty:
+            return None
 
         # Process each indicator, convert DataFrame to JSON-serializable format, and collect the results
         json_ready_results = {}
+
         for indicator in indicators.itertuples(index=False):
             indicator_results = self.process_indicator(indicator=indicator, num_results=num_results)
 
@@ -561,12 +576,8 @@ class Indicators:
         # Get the user ID to filter the indicators belonging to the user
         user_id = self.users.get_id(user_name)
 
-        # Remove the indicator from the DataFrame where the name matches and the creator is the user
+        identifying_values = [('name', indicator_name), ('creator', user_id)]
-        self.indicators = self.indicators[
+        self.cache_manager.remove_row_from_datacache(cache_name='indicators', filter_vals=identifying_values)
-            ~((self.indicators['name'] == indicator_name) & (self.indicators['creator'] == user_id))
-        ]
-
-        self.users.remove_indicator(indicator_name=indicator_name, user_name=user_name)
 
     def create_indicator(self, creator: str, name: str, kind: str,
                          source: dict, properties: dict, visible: bool = True):
@@ -583,36 +594,29 @@ class Indicators:
         :param visible: Whether to display it in the chart view.
         :return: None
         """
-        # Todo: Possible refactor to save without storing the indicator instance
 
-        self.indicators = self.indicators.reset_index(drop=True)
         creator_id = self.users.get_id(creator)
         # Check if an indicator with the same name already exists
-        existing_indicator = self.indicators.query('name == @name and creator == @creator_id')
+        indicators = self.cache_manager.get_rows_from_datacache('indicators', [('name', name), ('creator', creator_id)])
 
-        if not existing_indicator.empty:
+        if not indicators.empty:
             print(f"Indicator '{name}' already exists for user '{creator}'. Skipping creation.")
             return  # Exit the method to prevent duplicate creation
 
         if kind not in self.indicator_registry:
             raise ValueError(f"Requested an unsupported type of indicator: ({kind})")
 
-        indicator_class = self.indicator_registry[kind]
-        # Create an instance of the indicator.
-        indicator = indicator_class(name, kind, properties)
-
         # Add the new indicator to a pandas dataframe.
         creator_id = self.users.get_id(creator)
-        row_data = {
+        row_data = pd.DataFrame([{
             'creator': creator_id,
             'name': name,
             'kind': kind,
             'visible': visible,
             'source': source,
-            'properties': properties,
+            'properties': properties
-            'ref': indicator
+        }])
-        }
+        self.cache_manager.insert_df_into_datacache(df=row_data, cache_name="users", skip_cache=False)
-        self.indicators = pd.concat([self.indicators, pd.DataFrame([row_data])], ignore_index=True)
 
     # def update_indicators(self, user_name):
     #     """