Fix flake8 errors, docstrings, and add data provider docs

- Remove unused imports (F401) in alpha_vantage, polygon, yahoo, ohlcv_base
- Fix CSVOHLCVDataProvider __init__ docstring placement
- Fix PandasOHLCVDataProvider docstring: 'CSV file' -> 'pandas DataFrame'
- Add custom data providers documentation
- Update data sources docs with market info and links

Author: MDUYN

docusaurus/docs/Advanced Concepts/custom-data-providers.md

@@ -0,0 +1,347 @@
+---
+sidebar_position: 6
+---
+
+# Custom Data Providers
+
+Learn how the data provider system works and how to build your own data provider to integrate any data source into the framework.
+
+## How Data Providers Work
+
+The framework uses a **priority-based provider resolution** system. When a strategy declares a `DataSource`, the framework automatically finds the right data provider to fulfill it:
+
+1. **Registration** — All data providers are registered in a `DataProviderIndex`
+2. **Matching** — When a `DataSource` is declared, the framework calls `has_data()` on each registered provider
+3. **Priority** — If multiple providers match, the one with the lowest `priority` value wins
+4. **Instantiation** — The winning provider's `copy()` method creates a dedicated instance for that data source
+5. **Data retrieval** — The framework calls `get_data()` (live) or `get_backtest_data()` (backtesting) on the matched provider
+
+```
+DataSource("AAPL", market="YAHOO", time_frame="1d")
+                │
+                ▼
+      ┌─────────────────────┐
+      │  DataProviderIndex   │
+      │  ┌─────────────────┐ │
+      │  │  has_data()?    │ │  ← loops all registered providers
+      │  └─────────────────┘ │
+      └──────────┬──────────┘
+                 │
+    ┌────────────┼────────────┐
+    ▼            ▼            ▼
+  CCXT        Yahoo       Polygon
+  ✗            ✓            ✗
+                │
+                ▼
+         copy(data_source)
+                │
+                ▼
+       Dedicated instance
+       for AAPL / 1d / YAHOO
+```
+
+## Built-in Data Providers
+
+The framework ships with these OHLCV data providers:
+
+| Provider | Market | API Key Required | Supported Assets |
+|----------|--------|-----------------|------------------|
+| `CCXTOHLCVDataProvider` | Any CCXT exchange (e.g. `BINANCE`, `BITVAVO`) | Depends on exchange | Crypto |
+| `YahooOHLCVDataProvider` | `YAHOO` | No | Stocks, ETFs, indices, forex, crypto |
+| `AlphaVantageOHLCVDataProvider` | `ALPHA_VANTAGE` | Yes | Stocks, forex, crypto |
+| `PolygonOHLCVDataProvider` | `POLYGON` | Yes | US stocks, options, forex, crypto |
+| `CSVOHLCVDataProvider` | N/A | No | Any (from local CSV files) |
+| `PandasOHLCVDataProvider` | N/A | No | Any (from pandas DataFrames) |
+
+All OHLCV providers return data as [Polars](https://pola.rs/) DataFrames with columns: `Datetime`, `Open`, `High`, `Low`, `Close`, `Volume`.
+
+## Creating a Custom OHLCV Data Provider
+
+The easiest way to add a new data source is to extend `OHLCVDataProviderBase`. This base class handles all the boilerplate — storage caching, date range resolution, backtesting, `copy()` — and you only need to implement the API-specific download logic.
+
+### Minimal Example
+
+```python
+import polars as pl
+from datetime import datetime
+from investing_algorithm_framework import OHLCVDataProviderBase
+
+class MyBrokerOHLCVDataProvider(OHLCVDataProviderBase):
+    # The market string that DataSources will use
+    market_name = "MY_BROKER"
+
+    # Unique identifier for this provider
+    data_provider_identifier = "my_broker_ohlcv"
+
+    # Map framework timeframes to your API's format
+    timeframe_map = {
+        "1m": "1min",
+        "5m": "5min",
+        "1h": "60min",
+        "1d": "daily",
+    }
+
+    def _download_ohlcv(
+        self,
+        symbol: str,
+        time_frame,
+        start_date: datetime,
+        end_date: datetime,
+    ) -> pl.DataFrame:
+        """
+        Download OHLCV data from your broker's API.
+
+        Must return a Polars DataFrame with columns:
+        Datetime (timezone-aware UTC), Open, High, Low, Close, Volume
+        """
+        import my_broker_sdk
+
+        api_key = self._get_api_key()  # reads from MarketCredential
+        client = my_broker_sdk.Client(api_key)
+
+        interval = self._get_provider_interval()  # resolves from timeframe_map
+        raw_data = client.get_candles(
+            symbol=symbol,
+            interval=interval,
+            start=start_date.isoformat(),
+            end=end_date.isoformat(),
+        )
+
+        # Convert to the required DataFrame format
+        import pandas as pd
+        df = pd.DataFrame(raw_data)
+        df["Datetime"] = pd.to_datetime(df["timestamp"], utc=True)
+        df = df.rename(columns={
+            "open": "Open",
+            "high": "High",
+            "low": "Low",
+            "close": "Close",
+            "volume": "Volume",
+        })
+
+        return pl.from_pandas(
+            df[["Datetime", "Open", "High", "Low", "Close", "Volume"]]
+        )
+```
+
+### Using It
+
+```python
+from investing_algorithm_framework import (
+    create_app,
+    DataSource,
+    MarketCredential,
+    TradingStrategy,
+    TimeUnit,
+)
+
+app = create_app()
+
+# Register API credentials
+app.add_market_credential(
+    MarketCredential(
+        market="MY_BROKER",
+        api_key="your_api_key",
+    )
+)
+
+# Register the custom provider
+app.add_data_provider(MyBrokerOHLCVDataProvider())
+
+# Use it in a strategy
+class MyStrategy(TradingStrategy):
+    time_unit = TimeUnit.DAY
+    interval = 1
+    symbols = ["AAPL"]
+    trading_symbol = "USD"
+
+    data_sources = [
+        DataSource(
+            identifier="aapl_daily",
+            market="MY_BROKER",     # matches market_name
+            symbol="AAPL",
+            data_type="OHLCV",
+            time_frame="1d",        # must be in timeframe_map
+            warmup_window=50,
+        ),
+    ]
+```
+
+## OHLCVDataProviderBase Reference
+
+### Class Attributes
+
+| Attribute | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `market_name` | `str` | Yes | The market identifier string (e.g. `"MY_BROKER"`). DataSources match against this. |
+| `timeframe_map` | `dict` | Yes | Maps framework timeframe strings (`"1m"`, `"1d"`, etc.) to provider-specific values. |
+| `data_provider_identifier` | `str` | Yes | Unique identifier for this provider type. |
+
+### Methods to Override
+
+#### `_download_ohlcv()` (required)
+
+```python
+def _download_ohlcv(
+    self,
+    symbol: str,
+    time_frame,
+    start_date: datetime,
+    end_date: datetime,
+) -> pl.DataFrame:
+```
+
+Downloads OHLCV data from your external API. Must return a Polars DataFrame with columns `Datetime`, `Open`, `High`, `Low`, `Close`, `Volume`. The `Datetime` column must be timezone-aware UTC.
+
+Use `self._get_provider_interval()` to get the mapped interval value from `timeframe_map`.
+
+Use `self._get_api_key()` to retrieve the API key from the configured `MarketCredential`.
+
+#### `_validate_symbol()` (optional)
+
+```python
+def _validate_symbol(self, data_source: DataSource) -> bool:
+```
+
+Called during `has_data()` to validate whether the provider supports the requested symbol. Defaults to returning `True`. Override this if your API provides a way to verify symbol availability.
+
+#### `_storage_file_suffix()` (optional)
+
+```python
+def _storage_file_suffix(self) -> str:
+```
+
+Returns the suffix used for cached CSV file names. Defaults to `market_name.lower()`. Override if you need a different naming convention (e.g. `"alpha_vantage"` instead of `"alpha_vantage"`).
+
+### Inherited Methods (no override needed)
+
+These are handled automatically by the base class:
+
+- `has_data()` — checks market name, timeframe support, storage cache, and calls `_validate_symbol()`
+- `get_data()` — resolves date ranges, checks cache, calls `_download_ohlcv()`, handles storage
+- `prepare_backtest_data()` — downloads full range and caches for backtesting
+- `get_backtest_data()` — slices cached data by backtest index date and window
+- `copy()` — creates a dedicated provider instance for a matched DataSource
+- `get_number_of_data_points()` — calculates expected data points for a date range
+- `get_missing_data_dates()` — returns dates with missing data
+
+## Creating a Fully Custom Data Provider
+
+If you need to provide non-OHLCV data or need complete control, extend `DataProvider` directly. You must implement all abstract methods:
+
+```python
+from investing_algorithm_framework import DataProvider, DataType, DataSource
+
+class CustomSentimentDataProvider(DataProvider):
+    data_type = DataType.CUSTOM_DATA
+    data_provider_identifier = "sentiment_provider"
+
+    def has_data(self, data_source, start_date=None, end_date=None):
+        """Return True if this provider can serve the data source."""
+        return (
+            data_source.data_type == "CUSTOM_DATA"
+            and data_source.market == "SENTIMENT_API"
+        )
+
+    def get_data(self, date=None, start_date=None, end_date=None, save=False):
+        """Fetch live data."""
+        # Your API call here
+        return {"sentiment_score": 0.75, "volume_buzz": 1.2}
+
+    def prepare_backtest_data(
+        self, backtest_start_date, backtest_end_date,
+        fill_missing_data=False, show_progress=False,
+    ):
+        """Download and cache historical data for backtesting."""
+        self.data = self._fetch_historical(
+            backtest_start_date, backtest_end_date
+        )
+
+    def get_backtest_data(
+        self, backtest_index_date, backtest_start_date=None,
+        backtest_end_date=None, data_source=None,
+    ):
+        """Return data for a specific backtest date."""
+        return self.data.get(backtest_index_date)
+
+    def copy(self, data_source):
+        """Create a new instance configured for this data source."""
+        provider = CustomSentimentDataProvider()
+        provider.symbol = data_source.symbol
+        provider.market = data_source.market
+        return provider
+
+    def get_number_of_data_points(self, start_date, end_date):
+        return 0
+
+    def get_missing_data_dates(self, start_date, end_date):
+        return []
+
+    def get_data_source_file_path(self):
+        return None
+```
+
+## Provider Priority
+
+When multiple providers can serve the same DataSource, the framework picks the one with the lowest `priority` value:
+
+```python
+class PrimaryProvider(OHLCVDataProviderBase):
+    market_name = "STOCKS"
+    priority = 0  # highest priority (default)
+    ...
+
+class FallbackProvider(OHLCVDataProviderBase):
+    market_name = "STOCKS"
+    priority = 10  # lower priority, used as fallback
+    ...
+```
+
+Custom providers added via `app.add_data_provider()` receive a default priority of `3`. Built-in providers have a priority of `0`.
+
+## API Key Configuration
+
+Providers that require authentication use `MarketCredential`:
+
+```python
+from investing_algorithm_framework import MarketCredential
+
+app.add_market_credential(
+    MarketCredential(
+        market="MY_BROKER",       # must match provider's market_name
+        api_key="your_api_key",
+        secret_key="your_secret", # optional
+    )
+)
+```
+
+Inside your provider, call `self._get_api_key()` to retrieve the key. This reads from the `MarketCredential` whose `market` matches your provider's `market_name`.
+
+API keys can also be configured via environment variables. `MarketCredential` automatically reads `{MARKET}_API_KEY` and `{MARKET}_SECRET_KEY`:
+
+```bash
+export MY_BROKER_API_KEY=your_api_key
+export MY_BROKER_SECRET_KEY=your_secret
+```
+
+```python
+# This will auto-read from MY_BROKER_API_KEY env var
+app.add_market_credential(MarketCredential(market="MY_BROKER"))
+```
+
+## Storage and Caching
+
+`OHLCVDataProviderBase` automatically caches downloaded data as CSV files. Files are named using the pattern:
+
+```
+{symbol}_{timeframe}_{suffix}.csv
+```
+
+For example: `AAPL_1d_my_broker.csv`
+
+The storage directory is resolved in order:
+1. `storage_directory` passed to the constructor
+2. `storage_path` from the DataSource
+3. `RESOURCE_DIRECTORY/data/` from the app config
+
+To disable caching, don't configure a storage directory and don't set `save=True` on the DataSource.