### Complete PyFedWatch Workflow Example

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Demonstrates the full workflow from data loading to generating rate expectations, including all intermediate steps. This example requires setting up FOMC dates, Fed Funds rate history, and creating a FedWatch instance.

```python
import pyfedwatch as fw
from pyfedwatch.datareader import (
    read_price_history,
    get_fomc_data,
    get_fedfunds_range
)

# Step 1: Get FOMC meeting dates
fomc_data = get_fomc_data()
fomc_dates = fomc_data[
    (fomc_data['Status'] == 'Scheduled') | (fomc_data['Status'] == 'Cancelled')
].index.tolist()

# Step 2: Get Fed Funds target rate history (optional, for reference)
ff_range = get_fedfunds_range()

# Step 3: Create FedWatch instance
fedwatch = fw.fedwatch.FedWatch(
    watch_date='2023-03-10',
    fomc_dates=fomc_dates,
    num_upcoming=9,
    user_func=read_price_history,
    path='./data/contracts'
)
```

```python
# Step 4: Generate binary hike information (intermediate step)
binary_hike_info = fedwatch.generate_binary_hike_info()
print("Binary Hike Info:")
print(binary_hike_info[['Meeting', 'Pstart', 'Pavg', 'Pend', 'Change', 'H0', 'H1', 'P0', 'P1']])
```

```python
# Step 5: Generate final rate expectations
rate_expectations = fedwatch.generate_hike_info(rate_cols=True)
```

```python
# Step 6: Format and display results
styled_df = rate_expectations.style.format("{:.1%}").background_gradient(axis=1)
print("\nRate Expectations (probabilities):")
print(rate_expectations.to_string())
```

```python
# Step 7: Access FOMC calendar data
fomc_summary = fedwatch.fomc_data.summary
print("\nFOMC Calendar Summary:")
print(fomc_summary)
```

```python
# Step 8: Plot FOMC calendar
fig = fedwatch.fomc_data.plot_fomc_calendar()
fig.savefig('fomc_calendar.png', bbox_inches='tight', dpi=150)
```

--------------------------------

### Install PyFedWatch using pip

Source: https://github.com/arahimiquant/pyfedwatch/blob/master/README.md

Install the PyFedWatch package using pip. This command downloads and installs the latest version of the package and its dependencies.

```bash
pip install pyfedwatch
```

--------------------------------

### Get FOMC Meeting Data

Source: https://github.com/arahimiquant/pyfedwatch/blob/master/notebooks/00-pyfedwatch-user-guide.ipynb

Retrieve FOMC meeting data. Option 1 uses `read_fomc_data` which requires a path to local data. Option 2, `get_fomc_data`, is recommended as it fetches data directly and allows filtering for scheduled or cancelled meetings.

```python
# Get FOMC meetings data

# Option 1: Use read_fomc_data and create a list
fomc_data_1 = read_fomc_data(path = '../data/fomc')
fomc_dates_1 = fomc_data_1['FOMCDate'].to_list()
```

```python
# Option 2: (Recommended) Use get_fomc_data and create a list
fomc_data_2 = get_fomc_data()
fomc_dates_2 = fomc_data_2[(fomc_data_2['Status'] == 'Scheduled') | (fomc_data_2['Status'] == 'Cancelled')].index.tolist()
```

--------------------------------

### Get Fed Funds Target Rate for a Specific Date

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Access the Federal Funds target rate (lower and upper limits) for a specific date from the retrieved `ff_range` DataFrame.

```python
# Get target rate for a specific date
target_date = '2023-03-10'
rate_on_date = ff_range.loc[target_date]
print(f"Lower Limit: {rate_on_date['LL']}%, Upper Limit: {rate_on_date['UL']}%")
# Output: Lower Limit: 4.5%, Upper Limit: 4.75%
```

--------------------------------

### Get Fed Funds Target Rate Range

Source: https://github.com/arahimiquant/pyfedwatch/blob/master/notebooks/00-pyfedwatch-user-guide.ipynb

Retrieve the historical upper and lower limits for the Fed Funds target rate using `get_fedfunds_range`. The result is a pandas DataFrame indexed by date.

```python
# Get Fed Funds target rate upper and lower limits
ff_range = get_fedfunds_range()
ff_range
```

--------------------------------

### Initialize FedWatch with Custom Data Reader

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Shows how to create a `FedWatch` instance, providing a custom `user_func` for reading price history and specifying additional arguments for the function. It also demonstrates generating rate expectations with and without target rate columns.

```python
import pyfedwatch as fw
from pyfedwatch.datareader import read_price_history, get_fomc_data

# Get FOMC dates
fomc_data = get_fomc_data()
fomc_dates = fomc_data[
    (fomc_data['Status'] == 'Scheduled') | (fomc_data['Status'] == 'Cancelled')
].index.tolist()

# Create FedWatch instance with custom data reader function
# The user_func must accept 'symbol' as its first argument and return
# a DataFrame with 'Date' (as index or column) and 'Close' column
fedwatch = fw.fedwatch.FedWatch(
    watch_date='2023-03-10',
    fomc_dates=fomc_dates,
    num_upcoming=9,
    user_func=read_price_history,  # Your data reader function
    path='./data/contracts'         # Additional kwargs passed to user_func
)

# Generate rate expectations with target rate columns
rate_expectations = fedwatch.generate_hike_info(rate_cols=True)
print(rate_expectations)
# Output: DataFrame with FOMC dates as index and target rate ranges as columns
#                           4.50-4.75  4.75-5.00  5.00-5.25  5.25-5.50  ... 
# WatchDate  FOMCDate
# 2023-03-10 2023-03-22          0.0      0.222      0.778      0.000  ... 
#            2023-05-03          0.0      0.038      0.456      0.506  ... 
#            2023-06-14          0.0      0.017      0.297      0.480  ... 

# Generate with basis point changes instead of rate columns
rate_expectations_bp = fedwatch.generate_hike_info(rate_cols=False)

# Provide watch date rate range manually (if FRED unavailable)
rate_expectations = fedwatch.generate_hike_info(
    rate_cols=True,
    watch_rate_range=(4.50, 4.75)  # (lower_limit, upper_limit)
)
```

--------------------------------

### Initialize FOMC Calendar and View Summary

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Demonstrates how to fetch FOMC meeting dates, filter them, and initialize the `FOMC` class. It also shows how to access the summary DataFrame and individual lists of months, contracts, and meetings.

```python
import pyfedwatch as fw
from pyfedwatch.datareader import get_fomc_data

# Get FOMC meeting dates from Federal Reserve website and FRASER database
fomc_data = get_fomc_data()
# Filter for scheduled or cancelled meetings (exclude notation votes)
fomc_dates = fomc_data[
    (fomc_data['Status'] == 'Scheduled') | (fomc_data['Status'] == 'Cancelled')
].index.tolist()

# Create FOMC calendar for 9 upcoming meetings from March 10, 2023
fomc = fw.fomc.FOMC(
    watch_date='2023-03-10',
    fomc_dates=fomc_dates,
    num_upcoming=9
)

# View the summary DataFrame with contracts, meetings, and order
print(fomc.summary)
# Output:
#         Contract     Meeting  Order
# YYYY-MM
# 2023-01    ZQF23     No FOMC      0
# 2023-02    ZQG23  2023-02-01     -1
# 2023-03    ZQH23  2023-03-22      1
# 2023-05    ZQK23  2023-05-03      2
# 2023-06    ZQM23  2023-06-14      3
# ...

# Access individual components
print(fomc.month_list)      # ['2023-01', '2023-02', '2023-03', ...]
print(fomc.contract_list)   # ['ZQF23', 'ZQG23', 'ZQH23', ...]
print(fomc.meeting_list)    # ['No FOMC', '2023-02-01', '2023-03-22', ...]

# Plot the FOMC calendar visualization
# Blue boxes = FOMC meeting days, Red box = watch date
fig = fomc.plot_fomc_calendar()
fig.savefig('fomc_calendar.png', bbox_inches='tight', dpi=150)
```

--------------------------------

### FedWatch Initialization and Data Access

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Demonstrates how to initialize the FedWatch class with custom data readers and access summary data.

```APIDOC
## FedWatch Class Initialization and Data Access

### Description
This section covers accessing summary data from the FedWatch object and initializing the class with custom data reading functions.

### Accessing Summary Data

```python
# Access intermediate data
print(fedwatch.fomc_data.summary)  # Full summary with price data
print(fedwatch.watch_rate_range)    # Current target rate range
```

### Custom Data Reader Function

The FedWatch class requires a user-provided function to read fed funds futures pricing data. This function must accept `symbol` as its first argument and return a pandas DataFrame with a `Date` column (or index) and a `Close` column containing the settlement prices.

**Required DataFrame format:**
- Index: `Date` (datetime)
- Columns: Must include `Close` (float)

**Example DataFrame output:**
```
              Open     High      Low    Close  Volume
Date
2023-01-03  95.125  95.130  95.120  95.125    1000
2023-01-04  95.130  95.135  95.125  95.130    1500
```

**Example Custom Data Reader Functions:**

**1. Read from Excel files:**
```python
import pandas as pd

def read_from_excel(symbol: str, path: str) -> pd.DataFrame:
    """Read pricing data from Excel files."""
    return pd.read_excel(f'{path}/{symbol}.xlsx', index_col=0)
```

**2. Read from CSV files:**
```python
import pandas as pd

def read_from_csv(symbol: str, data_dir: str) -> pd.DataFrame:
    """Read pricing data from CSV files."""
    df = pd.read_csv(f'{data_dir}/{symbol}.csv')
    df['Date'] = pd.to_datetime(df['Date'])
    df.set_index('Date', inplace=True)
    return df
```

**3. Fetch from API (hypothetical):**
```python
import pandas as pd
import requests

def read_from_api(symbol: str, api_key: str, base_url: str) -> pd.DataFrame:
    """Fetch pricing data from a REST API."""
    response = requests.get(
        f'{base_url}/futures/{symbol}',
        headers={'Authorization': f'Bearer {api_key}'}
    )
    data = response.json()
    df = pd.DataFrame(data['prices'])
    df['Date'] = pd.to_datetime(df['date'])
    df.set_index('Date', inplace=True)
    df.rename(columns={'close': 'Close'}, inplace=True)
    return df[['Close']]
```

### Using Custom Function with FedWatch

```python
import pyfedwatch as fw

# Assuming fomc_dates is defined elsewhere
fomc_dates = [...] 

fedwatch = fw.fedwatch.FedWatch(
    watch_date='2023-03-10',
    fomc_dates=fomc_dates,
    num_upcoming=9,
    user_func=read_from_excel,  # Your custom function
    path='./data/contracts'      # Passed as **kwargs to user_func
)
```
```

--------------------------------

### Initialize FedWatch with Custom Data Reader

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Initialize the FedWatch class with a custom user-provided function for reading pricing data. The 'path' argument is passed as keyword arguments to the user function.

```python
# Using custom function with FedWatch
fedwatch = fw.fedwatch.FedWatch(
    watch_date='2023-03-10',
    fomc_dates=fomc_dates,
    num_upcoming=9,
    user_func=read_from_excel,  # Your custom function
    path='./data/contracts'      # Passed as **kwargs to user_func
)
```

--------------------------------

### FOMC Class Summary

Source: https://github.com/arahimiquant/pyfedwatch/blob/master/notebooks/00-pyfedwatch-user-guide.ipynb

Instantiate the FOMC class to view a summary of upcoming and past FOMC meetings. Requires a watch date, a list of FOMC dates, and the number of upcoming meetings to consider.

```python
# Testing FOMC class
fomc = fw.fomc.FOMC(watch_date = '2023-03-10',
                    fomc_dates = fomc_dates_2,
                    num_upcoming = 9)

fomc.summary
```

--------------------------------

### Import PyFedWatch Library

Source: https://github.com/arahimiquant/pyfedwatch/blob/master/notebooks/00-pyfedwatch-user-guide.ipynb

Import necessary modules from the PyFedWatch library for data reading and analysis.

```python
import pyfedwatch as fw
from pyfedwatch.datareader import read_price_history, get_fedfunds_range
from pyfedwatch.datareader import read_fomc_data, get_fomc_data
```

--------------------------------

### Calculate Rate Hike Expectations

Source: https://github.com/arahimiquant/pyfedwatch/blob/master/notebooks/00-pyfedwatch-user-guide.ipynb

Calculate and display rate hike information using the FedWatch class. This involves initializing the class with relevant dates and data paths, then generating hike information. The output is styled for better readability.

```python
# Calculate rate expectations
fedwatch = fw.fedwatch.FedWatch(watch_date = '2023-03-10',
                                fomc_dates = fomc_dates_2,
                                num_upcoming = 9,
                                user_func = read_price_history,
                                path = '../data/contracts')

fedwatch.generate_hike_info(rate_cols=True).style.format("{:.1%}").background_gradient(axis=1)
```

--------------------------------

### Generate FOMC Calendar

Source: https://github.com/arahimiquant/pyfedwatch/blob/master/README.md

Creates an FOMC calendar using the FOMC class. Requires watch_date, fomc_dates, and num_upcoming parameters.

```python
import pyfedwatch as fw
fomc = fw.fomc.FOMC(watch_date = '2023-03-10',
                    fomc_dates = fomc_dates,
                    num_upcoming = 9)
fig = fomc.plot_fomc_calendar()
```

--------------------------------

### Access Intermediate FedWatch Data

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Access summary and target rate range data from the FedWatch object. Ensure FedWatch is initialized with appropriate parameters.

```python
print(fedwatch.fomc_data.summary)  # Full summary with price data
print(fedwatch.watch_rate_range)    # Current target rate range
```

--------------------------------

### Custom API Data Reader Function (Hypothetical)

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Implement a function to fetch pricing data from a REST API. This function must accept a symbol, API key, and base URL, returning a pandas DataFrame with 'Date' and 'Close' columns.

```python
# Example 3: Fetch from API (hypothetical)
def read_from_api(symbol: str, api_key: str, base_url: str) -> pd.DataFrame:
    """Fetch pricing data from a REST API."""
    import requests
    response = requests.get(
        f'{base_url}/futures/{symbol}',
        headers={'Authorization': f'Bearer {api_key}'}
    )
    data = response.json()
    df = pd.DataFrame(data['prices'])
    df['Date'] = pd.to_datetime(df['date'])
    df.set_index('Date', inplace=True)
    df.rename(columns={'close': 'Close'}, inplace=True)
    return df[['Close']]
```

--------------------------------

### Calculate Rate Expectations

Source: https://github.com/arahimiquant/pyfedwatch/blob/master/README.md

Generates a dataframe of rate expectations for upcoming FOMC meetings. Requires watch_date, fomc_dates, num_upcoming, a user-defined function for price history, and a path to contract data.

```python
import pyfedwatch as fw
fedwatch = fw.fedwatch.FedWatch(watch_date = '2023-03-10',
                                fomc_dates = fomc_dates,
                                num_upcoming = 9,
                                user_func = read_price_history,
                                path = '../data/contracts')
fedwatch.generate_hike_info(rate_cols=True).style.format("{:.1%}").background_gradient(axis=1)
```

--------------------------------

### Read Fed Funds Futures Contract Pricing Data

Source: https://github.com/arahimiquant/pyfedwatch/blob/master/notebooks/00-pyfedwatch-user-guide.ipynb

Read historical Open, High, Low, Close, and Volume data for a specific Fed Funds futures contract using `read_price_history`. Requires the contract symbol and a path to the data.

```python
# Read one of the fed funds futures contracts pricing data
ohlc_sample = read_price_history(symbol='ZQJ22', path='../data/contracts')
ohlc_sample
```

--------------------------------

### Custom Excel Data Reader Function

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Implement a function to read pricing data from Excel files. This function must accept a symbol and path, returning a pandas DataFrame with 'Date' and 'Close' columns.

```python
import pandas as pd
import pyfedwatch as fw

# Example 1: Read from Excel files (provided in package)
def read_from_excel(symbol: str, path: str) -> pd.DataFrame:
    """Read pricing data from Excel files."""
    return pd.read_excel(f'{path}/{symbol}.xlsx', index_col=0)
```

--------------------------------

### Calculate Current Fed Rate Range

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Calculates the current Fed Funds target rate range for a given watch date using pre-loaded data.

```python
watch_date = '2023-03-10'
rate_range = (ff_range.loc[watch_date, 'LL'], ff_range.loc[watch_date, 'UL'])
print(f"Watch date rate range: {rate_range}")
```

--------------------------------

### Custom CSV Data Reader Function

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Implement a function to read pricing data from CSV files. This function must accept a symbol and data directory, returning a pandas DataFrame with 'Date' and 'Close' columns.

```python
# Example 2: Read from CSV files
def read_from_csv(symbol: str, data_dir: str) -> pd.DataFrame:
    """Read pricing data from CSV files."""
    df = pd.read_csv(f'{data_dir}/{symbol}.csv')
    df['Date'] = pd.to_datetime(df['Date'])
    df.set_index('Date', inplace=True)
    return df
```

--------------------------------

### Plot FOMC Calendar

Source: https://github.com/arahimiquant/pyfedwatch/blob/master/notebooks/00-pyfedwatch-user-guide.ipynb

Generate a visual representation of the FOMC calendar using the `plot_fomc_calendar` method from the FOMC class.

```python
# Plot FOMC calendar
fig = fomc.plot_fomc_calendar()
```

--------------------------------

### Retrieve FOMC Data from Federal Reserve Website

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Retrieve FOMC meeting data exclusively from the Federal Reserve website. This source includes upcoming meetings.

```python
# Get data from Federal Reserve website only (includes upcoming meetings)
fomc_fed = get_fomc_data_fed()
```

--------------------------------

### datareader.get_fedfunds_range - Retrieve Fed Funds Target Rate History

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Retrieves historical Federal Funds target rate lower and upper limits from the FRED database.

```APIDOC
## datareader.get_fedfunds_range - Retrieve Fed Funds Target Rate History

### Description
The `get_fedfunds_range` function retrieves historical Federal Funds target rate lower and upper limits from the FRED database. Before December 16, 2008, the Federal Reserve used a single target rate, so both limits are equal for those dates. After that date, the function returns the distinct target range that the Fed uses today.

### Method
`get_fedfunds_range()`

### Endpoint
N/A (Function call)

### Parameters
None

### Request Example
```python
from pyfedwatch.datareader import get_fedfunds_range

# Get historical Fed Funds target rate range
ff_range = get_fedfunds_range()
print(ff_range)
```

### Response
#### Success Response (DataFrame)
- **Date** (datetime index) - The date of the target rate.
- **LL** (float) - The lower limit of the Federal Funds target rate.
- **UL** (float) - The upper limit of the Federal Funds target rate.

#### Response Example
```
                LL     UL
Date
1982-09-27  10.25  10.25  # Single target rate (pre-2008)
1982-09-28  10.25  10.25
...
2008-12-16   0.00   0.25  # Target range begins
...
2023-09-19   5.25   5.50  # Current target range
```

### Usage Examples

**Get target rate for a specific date:**
```python
target_date = '2023-03-10'
rate_on_date = ff_range.loc[target_date]
print(f"Lower Limit: {rate_on_date['LL']}%, Upper Limit: {rate_on_date['UL']}%")
# Output: Lower Limit: 4.5%, Upper Limit: 4.75%
```
```

--------------------------------

### Read Futures Contract Pricing Data

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Reads Fed Funds futures contract pricing data from Excel files. Ensure contract files follow the CME Globex naming convention (ZQ + month code + year).

```python
from pyfedwatch.datareader import read_price_history

# Read pricing data for March 2023 contract (ZQH23)
# Month codes: F=Jan, G=Feb, H=Mar, J=Apr, K=May, M=Jun
#              N=Jul, Q=Aug, U=Sep, V=Oct, X=Nov, Z=Dec
ohlc = read_price_history(symbol='ZQH23', path='./data/contracts')
print(ohlc)
```

```python
# Access specific data
latest_close = ohlc.iloc[-1]['Close']
implied_rate = 100 - latest_close
print(f"Latest close: {latest_close}, Implied rate: {implied_rate:.4f}%")
```

```python
# Get data for a specific date
watch_date = '2023-03-10'
close_price = ohlc[ohlc.index <= watch_date].iloc[-1]['Close']
print(f"Close price on {watch_date}: {close_price}")
```

--------------------------------

### Retrieve Combined FOMC Meeting Data

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Retrieve historical and upcoming FOMC meeting dates from both Federal Reserve website and FRASER database. This is the recommended method.

```python
from pyfedwatch.datareader import get_fomc_data, get_fomc_data_fed, get_fomc_data_fraser

# Get combined FOMC data from both sources (recommended)
fomc_data = get_fomc_data()
print(fomc_data)
# Output:
#                     Status                          Days
# FOMCDate
# 1981-02-03       Scheduled       February 2-3, 1981
# 1981-03-31       Scheduled       March 31, 1981
# 2023-03-22       Scheduled       March 21-22, 2023
# 2024-01-31       Scheduled       January 30-31, 2024
# ...

```

--------------------------------

### Retrieve FOMC Data from FRASER Database

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Retrieve FOMC meeting data exclusively from the FRASER database. This source contains historical data and does not include upcoming meetings.

```python
# Get data from FRASER database only (historical, no upcoming)
fomc_fraser = get_fomc_data_fraser(decades=[1990, 2000, 2010, 2020])
```

--------------------------------

### datareader.get_fomc_data - Retrieve FOMC Meeting Dates

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Retrieves historical and upcoming FOMC meeting dates from various sources.

```APIDOC
## datareader.get_fomc_data - Retrieve FOMC Meeting Dates

### Description
The `get_fomc_data` function retrieves historical and upcoming FOMC meeting dates by combining data from the Federal Reserve website and the FRASER database. It returns a DataFrame with meeting dates, statuses (Scheduled, Unscheduled, Cancelled, Notation Vote), and a description of the meeting days. This is the recommended method for obtaining FOMC dates.

### Method
`get_fomc_data()`

### Endpoint
N/A (Function call)

### Parameters
None

### Request Example
```python
from pyfedwatch.datareader import get_fomc_data, get_fomc_data_fed, get_fomc_data_fraser

# Get combined FOMC data from both sources (recommended)
fomc_data = get_fomc_data()
print(fomc_data)
```

### Response
#### Success Response (DataFrame)
- **FOMCDate** (datetime index) - The date of the FOMC meeting.
- **Status** (string) - The status of the meeting (e.g., 'Scheduled', 'Unscheduled', 'Cancelled', 'Notation Vote').
- **Days** (string) - A description of the meeting days.

#### Response Example
```
                    Status                          Days
FOMCDate
1981-02-03       Scheduled       February 2-3, 1981
1981-03-31       Scheduled       March 31, 1981
2023-03-22       Scheduled       March 21-22, 2023
2024-01-31       Scheduled       January 30-31, 2024
...
```

### Related Functions

- **`get_fomc_data_fed()`**: Retrieves data from the Federal Reserve website only (includes upcoming meetings).
- **`get_fomc_data_fraser(decades=[...])`**: Retrieves data from the FRASER database only (historical, no upcoming meetings). Accepts a list of decades.
- **`read_fomc_data(path)`**: Reads FOMC data from a local file path.

### Usage Examples

**Filtering for scheduled meetings:**
```python
scheduled_meetings = fomc_data[fomc_data['Status'] == 'Scheduled']
fomc_dates = scheduled_meetings.index.tolist()
```

**Using local Excel file:**
```python
from pyfedwatch.datareader import read_fomc_data
fomc_local = read_fomc_data(path='./data/fomc')
fomc_dates_local = fomc_local['FOMCDate'].tolist()
```
```

--------------------------------

### Read FOMC Data from Local Excel File

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Read FOMC meeting data from a local Excel file using the `read_fomc_data` function. This provides an alternative to fetching data online.

```python
# Using with local Excel file (alternative)
from pyfedwatch.datareader import read_fomc_data
fomc_local = read_fomc_data(path='./data/fomc')
fomc_dates_local = fomc_local['FOMCDate'].tolist()
```

--------------------------------

### Retrieve Historical Fed Funds Target Rate Range

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Retrieve the historical Federal Funds target rate lower and upper limits from the FRED database. Before December 16, 2008, a single target rate was used.

```python
from pyfedwatch.datareader import get_fedfunds_range

# Get historical Fed Funds target rate range
ff_range = get_fedfunds_range()
print(ff_range)
# Output:
#                LL     UL
# Date
# 1982-09-27  10.25  10.25  # Single target rate (pre-2008)
# 1982-09-28  10.25  10.25
# ...
# 2008-12-16   0.00   0.25  # Target range begins
# ...
# 2023-09-19   5.25   5.50  # Current target range

```

--------------------------------

### Filter FOMC Data for Scheduled Meetings

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

Filter the FOMC data DataFrame to include only 'Scheduled' meetings and extract their dates into a list.

```python
# Filter for only scheduled meetings (exclude notation votes, cancelled)
scheduled_meetings = fomc_data[fomc_data['Status'] == 'Scheduled']
fomc_dates = scheduled_meetings.index.tolist()
```

--------------------------------

### Required DataFrame Format for Custom Reader

Source: https://context7.com/arahimiquant/pyfedwatch/llms.txt

The DataFrame returned by a custom data reader function must have a 'Date' index (datetime type) and a 'Close' column (float type).

```python
# Required DataFrame format:
# Index: Date (datetime)
# Columns: Must include 'Close' (float)
# Example output from user_func:
#               Open     High      Low    Close  Volume
# Date
# 2023-01-03  95.125  95.130  95.120  95.125    1000
# 2023-01-04  95.130  95.135  95.125  95.130    1500
```

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