### Setup Python Virtual Environment

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/stocks/build_trading_bot_with_ChatGPT/README.md

Install and activate a virtual environment to manage project dependencies.

```bash
sudo apt update
sudo apt install python3-venv -y
python3 -m venv venv
source venv/bin/activate
```

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

### Install and Import Alpaca-py Library

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-zero-dte.ipynb

Installs the alpaca-py package and imports necessary modules for trading and data access. Ensure you are using a paper account for this example.

```python
# Install or upgrade the package `alpaca-py` and import it
!python3 -m pip install --upgrade alpaca-py

import pandas as pd
import numpy as np
from scipy.stats import norm
import alpaca
from scipy.optimize import brentq
from datetime import datetime, time
from zoneinfo import ZoneInfo

from alpaca.trading.client import TradingClient
from alpaca.trading.requests import (
    MarketOrderRequest,
    GetOptionContractsRequest,
    MarketOrderRequest,
    OptionLegRequest,
    ClosePositionRequest,
)
from alpaca.data.historical.option import OptionHistoricalDataClient
from alpaca.data.historical.stock import StockHistoricalDataClient, StockLatestTradeRequest
from alpaca.data.requests import OptionLatestQuoteRequest
from alpaca.trading.enums import (
    AssetStatus,
    # ExerciseStyle,
    OrderSide,
    OrderClass,
    OrderType,
    TimeInForce,
    # QueryOrderStatus,
    ContractType
)
```

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

### Example .env file for Local Development

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-zero-dte-backtesting/README.md

This is an example of how to structure your .env file to store API keys and other configuration settings for local development. Ensure this file is added to your .gitignore to prevent accidental commits.

```bash
# Edit .env with your API keys
ALPACA_API_KEY=your_alpaca_api_key
ALPACA_SECRET_KEY=your_alpaca_secret_key
ALPACA_PAPER_TRADE=True
DATABENTO_API_KEY=your_databento_api_key
```

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

### Install Alpaca-py Library

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-trading-basic.ipynb

Check for the library installation and install it if missing.

```python
# install alpaca-py if it is not available
try:
    import alpaca
except ImportError:
    !python3 -m pip install alpaca-py
    import alpaca
```

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

### Install Dependencies

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-zero-dte-backtesting/options-zero-dte-backtesting.ipynb

Install the required Python packages for the backtesting environment.

```bash
!uv pip install databento alpaca-py python-dotenv pandas numpy scipy matplotlib jupyter ipykernel
```

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

### Install Serverless Framework and Login

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/stocks/lbr-anti-setup-trading-bot/docs/PRE-DEPLOYMENT.md

Install the Serverless Framework and log in to your account. This is necessary for deploying and managing serverless applications, including the trading bot.

```bash
# 4. Install Serverless and log in
npm ci && npx serverless login
```

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

### Install Dependencies

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/stocks/build_trading_bot_with_ChatGPT/README.md

Install the required Python packages listed in requirements.txt.

```bash
pip install -r requirements.txt
```

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

### Install Dependencies using uv

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-zero-dte-backtesting/README.md

This command installs project dependencies using uv, a modern and faster package installer, creating a virtual environment named 'myvenv'. Ensure uv is installed separately before running this command.

```bash
uv venv myvenv
source myvenv/bin/activate # On Windows: myvenv\Scripts\activate
uv pip install databento alpaca-py python-dotenv pandas numpy scipy matplotlib jupyter ipykernel
```

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

### Install and Import Libraries

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-polygon-alpaca.ipynb

Installs and imports necessary Python libraries for data analysis and API interaction. Ensure you have the correct Python version installed.

```python
# Install or upgrade the package `polygon-api-client` and `plotly` and import them
!python3 -m pip install --upgrade polygon-api-client
!python3 -m pip install --upgrade plotly

#import modules
from polygon import RESTClient
import datetime as dt
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.offline import plot
from datetime import datetime, timedelta
```

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

### Install and Import alpaca-py

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-wheel-strategy.ipynb

Install the required library and import necessary modules for trading and data retrieval.

```python
!python3 -m pip install --upgrade alpaca-py

import pandas as pd
import numpy as np
from scipy.stats import norm
import alpaca
import time
from scipy.optimize import brentq
from datetime import datetime, timedelta
from zoneinfo import ZoneInfo
from alpaca.data.timeframe import TimeFrame, TimeFrameUnit


from alpaca.trading.client import TradingClient
from alpaca.data.historical.option import OptionHistoricalDataClient
from alpaca.data.historical.stock import StockHistoricalDataClient, StockLatestTradeRequest
from alpaca.data.requests import StockBarsRequest, OptionLatestQuoteRequest, OptionChainRequest
from alpaca.trading.requests import GetOptionContractsRequest, MarketOrderRequest
from alpaca.trading.enums import AssetStatus, ContractType
```

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

### Install Dependencies using pip

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-zero-dte-backtesting/README.md

This command installs project dependencies using pip, creating a virtual environment named 'myvenv'. It includes essential libraries for Alpaca trading, data handling, and development.

```bash
python3 -m venv myvenv
source myvenv/bin/activate  # On Windows: myvenv\Scripts\activate
pip install databento alpaca-py python-dotenv pandas numpy scipy matplotlib jupyter ipykernel
```

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

### Install and Import Dependencies

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/crypto/crypto-trading-basic.ipynb

Ensure the alpaca-py library is installed and import necessary modules for trading and data operations.

```python
# install alpaca-py if it is not available
try:
    import alpaca
except ImportError:
    !python3 -m pip install alpaca-py
    import alpaca
```

```python
import json
from datetime import datetime, timedelta
from zoneinfo import ZoneInfo

from alpaca.trading.client import TradingClient
from alpaca.data.timeframe import TimeFrame, TimeFrameUnit
from alpaca.data.historical.crypto import CryptoHistoricalDataClient
from alpaca.trading.stream import TradingStream
from alpaca.data.live.crypto import CryptoDataStream

from alpaca.data.requests import (
    CryptoBarsRequest,
    CryptoQuoteRequest,
    CryptoTradesRequest,
    CryptoLatestQuoteRequest
    )
from alpaca.trading.requests import (
    GetAssetsRequest,
    MarketOrderRequest,
    LimitOrderRequest,
    StopLimitOrderRequest,
    GetOrdersRequest,
    ClosePositionRequest
)
from alpaca.trading.enums import (
    AssetClass,
    AssetStatus,
    OrderSide,
    OrderType,
    TimeInForce,
    QueryOrderStatus
)
from alpaca.common.exceptions import APIError
```

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

### Install and Import Alpaca-py Library

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-iron-butterfly.ipynb

Installs and imports necessary libraries for Alpaca trading and data analysis. Ensure you have the `alpaca-py` package installed or upgraded.

```python
# Install or upgrade the package `alpaca-py` and import it
# !python3 -m pip install --upgrade alpaca-py

import pandas as pd
import numpy as np
import alpaca
import time
from datetime import datetime, timedelta
from zoneinfo import ZoneInfo
from alpaca.data.timeframe import TimeFrame, TimeFrameUnit
from dotenv import load_dotenv
import os
from typing import Any, Dict, List, Optional, Tuple

from alpaca.data.historical.option import OptionHistoricalDataClient
from alpaca.data.historical.stock import StockHistoricalDataClient, StockLatestTradeRequest
from alpaca.data.requests import StockBarsRequest, OptionSnapshotRequest, OptionBarsRequest
from alpaca.trading.client import TradingClient
from alpaca.trading.requests import (
    MarketOrderRequest,
    GetOptionContractsRequest,
    OptionLegRequest,
    ClosePositionRequest,
)
from alpaca.trading.enums import (
    AssetStatus,
    ExerciseStyle,
    OrderSide,
    OrderClass,
    OrderStatus,
    OrderType,
    TimeInForce,
    QueryOrderStatus,
    ContractType,
)
```

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

### Install alpaca-py

Source: https://github.com/alpacahq/alpaca-py/blob/master/README.md

Install the alpaca-py library using pip. Ensure you are using Python 3.8+.

```shell
pip install alpaca-py
```

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

### Verify Virtual Environment Installation

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-zero-dte-backtesting/README.md

This command lists the contents of the virtual environment's bin directory, useful for verifying that the installation was successful and to locate the Python interpreter path.

```bash
ls -la /path/to/your/project/myvenv/bin/
```

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

### 0DTE Bull Put Spread Algorithm Setup

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-zero-dte-backtesting/options-zero-dte-backtesting.ipynb

This comment block outlines the setup and core functionalities of the 0DTE bull put spread algorithm. It highlights the selection of option contracts based on delta criteria and spread width, and the minute-by-minute monitoring of the position for exit conditions.

```python
# Complete 0DTE bull put spread algorithm demonstration:
# - Selects option contracts based on delta criteria and spread width
# - Monitors position every minute for exit conditions

```

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

### Install and Import Alpaca Trading Libraries

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-polygon-alpaca.ipynb

Installs and imports the necessary `alpaca-py` modules for trading operations, including clients for trading, options, and historical data.

```python
# Install or upgrade the package `alpaca-py` and import it
!python3 -m pip install --upgrade alpaca-py

import alpaca
from alpaca.trading.client import TradingClient
from alpaca.trading.requests import GetOptionContractsRequest, MarketOrderRequest
from alpaca.data.requests import OptionLatestQuoteRequest, OptionSnapshotRequest
from alpaca.data.historical.option import OptionHistoricalDataClient
from alpaca.data.historical.stock import StockHistoricalDataClient, StockLatestTradeRequest
```

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

### Install and Import Alpaca SDK

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-calendar-spread.ipynb

Installs the latest version of the Alpaca Python SDK and imports necessary modules for data retrieval, trading, and statistical calculations.

```python
!python3 -m pip install --upgrade alpaca-py
import pandas as pd
import numpy as np
from scipy.stats import norm
import alpaca
import time
from scipy.optimize import brentq
from datetime import datetime, timedelta
from zoneinfo import ZoneInfo
from alpaca.data.timeframe import TimeFrame, TimeFrameUnit

from alpaca.data.historical.option import OptionHistoricalDataClient
from alpaca.data.historical.stock import StockHistoricalDataClient, StockLatestTradeRequest
from alpaca.data.requests import StockBarsRequest, OptionLatestQuoteRequest, OptionChainRequest, OptionBarsRequest
from alpaca.trading.client import TradingClient
from alpaca.trading.requests import (
    MarketOrderRequest,
    GetOptionContractsRequest,
    MarketOrderRequest,
    OptionLegRequest,
    ClosePositionRequest,
)
from alpaca.trading.enums import (
    AssetStatus,
    ExerciseStyle,
    OrderSide,
    OrderClass,
    OrderStatus,
    OrderType,
    TimeInForce,
    QueryOrderStatus,
    ContractType
)
```

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

### Install and Import Alpaca-py Library

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-iron-condor.ipynb

Installs or upgrades the alpaca-py package and imports necessary libraries for data analysis and trading.

```python
# Install or upgrade the package `alpaca-py` and import it
!python3 -m pip install --upgrade alpaca-py

import pandas as pd
import numpy as np
from scipy.stats import norm
import alpaca
import time
from scipy.optimize import brentq
from datetime import datetime, timedelta
from zoneinfo import ZoneInfo
from alpaca.data.timeframe import TimeFrame, TimeFrameUnit

from alpaca.data.historical.option import OptionHistoricalDataClient
from alpaca.data.historical.stock import StockHistoricalDataClient, StockLatestTradeRequest
from alpaca.data.requests import StockBarsRequest, OptionLatestQuoteRequest, OptionChainRequest, OptionBarsRequest
from alpaca.trading.client import TradingClient
from alpaca.trading.requests import (
    MarketOrderRequest,
    GetOptionContractsRequest,
    MarketOrderRequest,
    OptionLegRequest,
    ClosePositionRequest,
)
from alpaca.trading.enums import (
    AssetStatus,
    ExerciseStyle,
    OrderSide,
    OrderClass,
    OrderStatus,
    OrderType,
    TimeInForce,
    QueryOrderStatus,
    ContractType,
)
```

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

### Install Alpaca-py and Dependencies

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/stocks/lbr-anti-setup-trading-bot/notebooks/ibr_algorithm_walkthrough.ipynb

Installs the necessary Python libraries for interacting with Alpaca's API, handling data, and plotting. This command is typically run in a notebook environment.

```bash
!uv pip install alpaca-py python-dotenv pandas matplotlib pytz
```

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

### Run Educational Notebook Locally

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/stocks/lbr-anti-setup-trading-bot/README.md

Launch the Jupyter Notebook for a step-by-step walkthrough of the LBR signal logic. Setup instructions are provided within the notebook.

```bash
jupyter notebook notebooks/ibr_algorithm_walkthrough.ipynb
```

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

### Get Corporate Actions

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/stocks/stocks-trading-basic.ipynb

Fetches corporate actions data for a given symbol starting from a specified date. Requires `CorporateActionsClient` and `CorporateActionsRequest`.

```python
from alpaca.trading.client import CorporateActionsClient
from alpaca.trading.requests import CorporateActionsRequest
from datetime import datetime

corporate_actions_client = CorporateActionsClient(api_key, secret_key)
corporate_actions_client.get_corporate_actions(CorporateActionsRequest(
    start=datetime(2020, 1, 1),
    symbols=[symbol]
)).df
```

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

### Get Options Historical Trades

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-trading-basic.ipynb

Retrieve historical options trades for a specified symbol. You can set the start and end times, and a limit for the results.

```python
# get options historical trades by symbol
req = OptionTradesRequest(
    symbol_or_symbols = high_open_interest_contract.symbol,
    start = now - timedelta(days = 5),                              # specify start datetime, default=the beginning of the current day.
    # end=None,                                                     # specify end datetime, default=now
    limit = 2,                                                      # specify limit
)
option_historical_data_client.get_option_trades(req).df
```

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

### Set Up Strategy Inputs and Parameters

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-bull-call-spread.ipynb

Initializes strategy parameters including underlying symbol, timezone, date range, strike price range, buying power limits, risk-free rate, and option expiration.

```python
# Select the underlying stock
underlying_symbol = 'WMT'

# Set the timezone
timezone = ZoneInfo('America/New_York')

# Get current date in US/Eastern timezone
today = datetime.now(timezone).date()

# Define a 6% range around the underlying price 
STRIKE_RANGE = 0.06

# Buying power percentage to use for the trade
BUY_POWER_LIMIT = 0.05

# Risk free rate for the options greeks and IV calculations
risk_free_rate = 0.01

# Check account buying power
# buying_power = float(trade_client.get_account().buying_power)

# Set the open interest volume threshold
OI_THRESHOLD = 50

# Calculate the limit amount of buying power to use for the trade
# buying_power_limit = buying_power * BUY_POWER_LIMIT

# Set the expiration date range for the options
min_expiration = today + timedelta(days=21)
max_expiration = today + timedelta(days=60)

# Get the latest price of the underlying stock
def get_underlying_price(symbol):
    # Get the latest trade for the underlying stock
    # underlying_trade_request = StockLatestTradeRequest(symbol_or_symbols=symbol)
    # underlying_trade_response = stock_data_client.get_stock_latest_trade(underlying_trade_request)
    # return underlying_trade_response[symbol].price
    pass # Placeholder for actual implementation

# Get the latest price of the underlying stock
# underlying_price = get_underlying_price(underlying_symbol)

# Set the minimum and maximum strike prices based on the underlying price
# min_strike = str(underlying_price * (1 - STRIKE_RANGE))
# max_strike = str(underlying_price * (1 + STRIKE_RANGE))

# Define the criteria for selecting the options

```

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

### Initialize Environment and Clients

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/crypto/crypto-trading-basic.ipynb

Setup the trading client and environment for Jupyter notebook execution.

```python
# to run async code in jupyter notebook
import nest_asyncio
nest_asyncio.apply()
```

```python
# check version of alpaca-py
alpaca.__version__
```

```python
# setup clients
trade_client = TradingClient(api_key=api_key, secret_key=secret_key, paper=paper, url_override=trade_api_url)
```

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

### Get Options Historical Bars

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-trading-basic.ipynb

Retrieve historical options bars for a given symbol. You can specify the timeframe, start and end dates, and a limit.

```python
# get options historical bars by symbol
req = OptionBarsRequest(
    symbol_or_symbols = high_open_interest_contract.symbol,
    timeframe = TimeFrame(amount = 1, unit = TimeFrameUnit.Hour),   # specify timeframe
    start = now - timedelta(days = 5),                              # specify start datetime, default=the beginning of the current day.
    # end_date=None,                                                # specify end datetime, default=now
    limit = 2,                                                      # specify limit
)
option_historical_data_client.get_option_bars(req).df
```

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

### Get Put Options Contracts for SPY

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-trade-options-with-alpaca.ipynb

Retrieves a list of put options contracts for the SPY underlying symbol. Note that this example is for demonstration and not investment advice.

```python
# Get put options contracts
# Please note that the stock SPY is used as an example and should not be considered investment advice.

req = GetOptionContractsRequest(
    underlying_symbol=['SPY'],
    type=ContractType.PUT
)

res = trade_client.get_option_contracts(req)
res
```

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

### Setup Historical Data Client

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/stocks/stocks-trading-basic.ipynb

Initializes the client for fetching historical stock data.

```python
# setup stock historical data client
stock_historical_data_client = StockHistoricalDataClient(api_key, secret_key, url_override = data_api_url)
```

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

### Create Project Directory

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/stocks/build_trading_bot_with_ChatGPT/README.md

Initialize a new directory for the trading bot project.

```bash
mkdir new_folder_name
cd ./new_folder_name
```

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

### Get Positions Filtered by Option Contract Symbol

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-trading-basic.ipynb

Fetches all open positions and filters them to show only those matching the specified option contract symbol. This example is best run during market hours as positions may not be visible otherwise.

```python
positions = trade_client.get_all_positions()
[pos for pos in positions if pos.symbol == high_open_interest_contract.symbol]
```

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

### Initialize Trading Client

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-trading-mleg.ipynb

Create a TradingClient instance using the configured credentials.

```python
# setup client
trade_client = TradingClient(api_key=api_key, secret_key=secret_key, paper=paper, url_override=trade_api_url)
```

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

### Install Alpaca-py and Pandas-ta

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/crypto/crypto-btc-usd-swing-trade.ipynb

Installs or upgrades the `alpaca-py` and `pandas-ta` Python packages. Ensure you have Python 3 installed.

```python
# Install or upgrade the package `alpaca-py` and import it
# !python3 -m pip install --upgrade alpaca-py
# Install or upgrade the package `pandas-ta`
# !python3 -m pip install -U git+https://github.com/twopirllc/pandas-ta
```

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

### Initialize Trading Client

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-trading-basic.ipynb

Create a TradingClient instance using the configured credentials.

```python
# setup clients
trade_client = TradingClient(api_key=api_key, secret_key=secret_key, paper=paper, url_override=trade_api_url)
```

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

### Install Node.js Dependencies

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/stocks/lbr-anti-setup-trading-bot/docs/PRE-DEPLOYMENT.md

Install Node.js dependencies using `npm ci`. This command installs exact versions from `package-lock.json`, ensuring consistency.

```bash
npm ci
```

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

### Setup Crypto Historical Data Client

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/crypto/crypto-trading-basic.ipynb

Initializes the client for fetching historical crypto market data.

```python
# setup crypto historical data client
crypto_historical_data_client = CryptoHistoricalDataClient()

symbol="BTC/USD"
```

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

### Main Trading Loop Setup and Logic

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/stocks/build_trading_bot_with_ChatGPT/trading_bot_chatgpt.ipynb

Sets up logging, initializes strategy variables, and enters a continuous loop to monitor market status, fetch data, compute indicators, and determine trading actions. Handles market open/close transitions and fetches historical bars for analysis.

```python
def main():
    """Main trading loop and setup."""
    # Configure logging
    logging.basicConfig(
        filename="trade_log.txt",          # file to write
        level=logging.INFO,                # log INFO and above
        format="%(asctime)s %(levelname)s: %(message)s",
        datefmt="%Y-%m-%d %H:%M:%S"
    )
    logging.info("=== Strategy started ===")

    # remembers whether the market was open in the previous iteration
    market_open = False

    # Set tracking signal flags over a predefined window
    global rsi_bounce_bar, macd_cross_bar, rsi_retreat_bar, macd_death_cross_bar, macd_centerline_bar, current_bar_index
    rsi_bounce_bar = None
    macd_cross_bar = None
    rsi_retreat_bar = None
    macd_death_cross_bar = None
    macd_centerline_bar = None
    current_bar_index = 0

    while True:
        clock = trade_client.get_clock()

        # The following code is commented out for running in the notebook. To run in `strategy.py`, uncomment the following code to use the market open/close logic
        
        # # Detect if the market has just transitioned from open to closed.
        # if market_open and not clock.is_open:
        #     logging.info("Market closed. Sleeping until next open at %s", clock.next_open)
        #     market_open = False
        #     sleep_until(clock.next_open)
        #     continue        # skip the rest of the loop while the market is shut

        # Detect if the market has just transitioned from closed to open.
        if (not market_open) and clock.is_open:
            logging.info("Market opened. Resuming trading")
            market_open = True           # fall through and run the trading logic

        # Detect if the market is closed (e.g., at script start or unexpected state), exit to prevent trading.
        if not clock.is_open:
            logging.info("Market is closed. Exiting.")
            exit(0)
        
        # Fetch data
        df_main = fetch_bars(stock_data_client, underlying_symbol, TIMEFRAME_MAIN, days=300)
        df_trend = fetch_bars(stock_data_client, underlying_symbol, TIMEFRAME_TREND, days=MA_SLOW + 10)
        logging.info("Fetched %d main bars and %d trend bars", len(df_main), len(df_trend))
        
        # Update current bar index
        current_bar_index = len(df_main) - 1

        # Check if we currently hold the underlying_symbol
        try:
            position = trade_client.get_open_position(underlying_symbol)
            position_open = True
            current_qty = int(position.qty)
        except Exception as e:
            position_open = False
            current_qty = 0

        # Compute indicators using helper functions
        prices = df_main.close
        rsi_series = compute_rsi(prices, RSI_PERIOD)
        macd_line, signal_line = compute_macd(prices, MACD_FAST, MACD_SLOW, MACD_SIGNAL)

        # Get latest values
        rsi_now = rsi_series.iloc[-1]
        rsi_prev = rsi_series.iloc[-2]
        macd_now = macd_line.iloc[-1]
        macd_prev = macd_line.iloc[-2]
        sig_now = signal_line.iloc[-1]
        sig_prev = signal_line.iloc[-2]

        # Trend filter on higher timeframe with NaN check
        ma_fast = df_trend.close.rolling(MA_FAST).mean()
        ma_mid = df_trend.close.rolling(MA_MID).mean()
        ma_slow = df_trend.close.rolling(MA_SLOW).mean()
        
        # Check if we have enough data for all MAs
        if not (ma_fast.isna().any() or ma_mid.isna().any() or ma_slow.isna().any()):
            in_uptrend = (ma_fast.iloc[-1] > ma_mid.iloc[-1]) and (ma_mid.iloc[-1] > ma_slow.iloc[-1])
        else:
            in_uptrend = False

        # Calculate position size based on buying power
        buying_power_limit = calculate_buying_power_limit(BUY_POWER_LIMIT)
        current_price = get_underlying_price(underlying_symbol)
        position_size = int(buying_power_limit / current_price)

        # Detect RSI oversold bounce
        if (rsi_prev < 30) and (rsi_now > 30):
            rsi_bounce_bar = current_bar_index

        # Detect MACD golden cross
        if (macd_prev < sig_prev) and (macd_now > sig_now):
            macd_cross_bar = current_bar_index

        # Entry logic
        if not position_open and in_uptrend and position_size > 0:

```

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

### Initialize BrokerClient

Source: https://github.com/alpacahq/alpaca-py/blob/master/docs/broker.rst

Instantiate the BrokerClient using API keys. Set the sandbox parameter to True to use the sandbox environment.

```python
from alpaca.broker import BrokerClient

BROKER_API_KEY = "api-key"
BROKER_SECRET_KEY = "secret-key"

broker_client = BrokerClient(
                    api_key=Broker_API_KEY,
                    secret_key=BROKER_SECRET_KEY,
                    sandbox=True,
                )
```

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

### Install and Import Alpaca-Py

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-bull-call-spread.ipynb

Installs or upgrades the alpaca-py package and imports necessary libraries for trading and data analysis. Ensure you have Python 3 installed.

```python
# Install or upgrade the package `alpaca-py` and import it
!python3 -m pip install --upgrade alpaca-py

import pandas as pd
import numpy as np
from scipy.stats import norm
import time
from scipy.optimize import brentq
from datetime import datetime, timedelta
from zoneinfo import ZoneInfo
from typing import Any, Dict, List, Optional, Tuple

import alpaca
from alpaca.data.timeframe import TimeFrame, TimeFrameUnit
from alpaca.data.historical.option import OptionHistoricalDataClient
from alpaca.data.historical.stock import StockHistoricalDataClient, StockLatestTradeRequest
from alpaca.data.requests import StockBarsRequest, OptionLatestQuoteRequest, OptionSnapshotRequest
from alpaca.trading.client import TradingClient
from alpaca.trading.requests import (
    MarketOrderRequest,
    GetOptionContractsRequest,
    MarketOrderRequest,
    OptionLegRequest,
    ClosePositionRequest,
)
from alpaca.trading.enums import (
    AssetStatus,
    OrderSide,
    OrderClass,
    OrderStatus,
    TimeInForce,
    ContractType,
)
```

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

### Initialize Trading Clients

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/stocks/build_trading_bot_with_ChatGPT/trading_bot_chatgpt.ipynb

Creates instances of the TradingClient and StockHistoricalDataClient using the loaded credentials.

```python
# setup trading clients
trade_client = TradingClient(api_key=API_KEY, secret_key=API_SECRET, paper=ALPACA_PAPER_TRADE, url_override=trade_api_url)
stock_data_client = StockHistoricalDataClient(api_key=API_KEY, secret_key=API_SECRET)
```

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

### Initialize Live Data Stream Clients

Source: https://github.com/alpacahq/alpaca-py/blob/master/docs/market_data.rst

Initializes clients for subscribing to real-time data streams for crypto, stocks, and options. API keys are required for live data.

```python
from alpaca.data.live import CryptoDataStream, OptionDataStream, StockDataStream

# keys are required for live data
crypto_stream = CryptoDataStream("api-key", "secret-key")

# keys required
stock_stream = StockDataStream("api-key", "secret-key")
option_stream = OptionDataStream("api-key", "secret-key")
```

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

### Install and Import Alpaca-Py and Libraries

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-long-straddle.ipynb

Installs the alpaca-py package and imports essential libraries for data manipulation, calculations, and Alpaca API interaction. Ensure you have Python 3 and pip installed.

```python
# Install or upgrade the package `alpaca-py` and import it
!python3 -m pip install --upgrade alpaca-py

import pandas as pd
import numpy as np
from scipy.stats import norm
import alpaca
import time
from scipy.optimize import brentq
from datetime import datetime, timedelta
from zoneinfo import ZoneInfo
from alpaca.data.timeframe import TimeFrame, TimeFrameUnit

from alpaca.data.historical.option import OptionHistoricalDataClient
from alpaca.data.historical.stock import StockHistoricalDataClient, StockLatestTradeRequest
from alpaca.data.requests import StockBarsRequest, OptionLatestQuoteRequest, OptionChainRequest, OptionBarsRequest
from alpaca.trading.client import TradingClient
from alpaca.trading.requests import (
    MarketOrderRequest,
    LimitOrderRequest,
    GetOptionContractsRequest,
    MarketOrderRequest,
    OptionLegRequest,
    ClosePositionRequest,
    CreateWatchlistRequest
)
from alpaca.trading.enums import (
    AssetStatus,
    ExerciseStyle,
    OrderSide,
    OrderClass,
    OrderStatus,
    OrderType,
    TimeInForce,
    QueryOrderStatus,
    ContractType,
)
```

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

### Initialize Alpaca Trading and Data Clients

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-polygon-alpaca.ipynb

Sets up the TradingClient and StockHistoricalDataClient using API keys and paper trading environment. Ensure your API keys are securely stored and accessible.

```python
from google.colab import userdata
API_KEY = userdata.get('ALPACA_API_KEY')
API_SECRET = userdata.get('ALPACA_SECRET_KEY')
BASE_URL = None
## We use paper environment for this example
PAPER = True # Please do not modify this. This example is for paper trading only.

trade_client = TradingClient(api_key=API_KEY, secret_key=API_SECRET, paper=PAPER, url_override=BASE_URL)
stock_data_client = StockHistoricalDataClient(api_key=API_KEY, secret_key=API_SECRET)
option_historical_data_client = OptionHistoricalDataClient(api_key=API_KEY, secret_key=API_SECRET, url_override=BASE_URL)
```

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

### Initialize Trading Client for Paper Trading

Source: https://github.com/alpacahq/alpaca-py/blob/master/docs/trading.rst

Instantiate the TradingClient with paper=True to enable the paper trading sandbox environment. Ensure your API keys correspond to a paper account.

```python
from alpaca.trading.client import TradingClient

# paper=True enables paper trading
trading_client = TradingClient('api-key', 'secret-key', paper=True)
```

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

### Install and Import Alpaca-py Libraries

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-bull-put-spread.ipynb

Installs and imports essential libraries for Alpaca trading and data access, including pandas, numpy, and various Alpaca-specific modules for historical data, trading, and request formatting. Ensure you have the latest version of alpaca-py installed.

```python
# Install or upgrade the package `alpaca-py` and import it
# !python3 -m pip install --upgrade alpaca-py

import pandas as pd
import numpy as np
from scipy.stats import norm
import time
from scipy.optimize import brentq
from datetime import datetime, timedelta
from zoneinfo import ZoneInfo
from dotenv import load_dotenv
import os
from typing import Any, Dict, List, Optional, Tuple

import alpaca
from alpaca.data.timeframe import TimeFrame, TimeFrameUnit
from alpaca.data.historical.option import OptionHistoricalDataClient
from alpaca.data.historical.stock import StockHistoricalDataClient, StockLatestTradeRequest
from alpaca.data.requests import StockBarsRequest, OptionLatestQuoteRequest, OptionSnapshotRequest
from alpaca.trading.client import TradingClient
from alpaca.trading.requests import (
    MarketOrderRequest,
    GetOptionContractsRequest,
    MarketOrderRequest,
    OptionLegRequest,
    ClosePositionRequest,
)
from alpaca.trading.enums import (
    AssetStatus,
    OrderSide,
    OrderClass,
    OrderStatus,
    TimeInForce,
    ContractType,
)
```

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

### Install and Update Alpaca-py

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-trade-options-with-alpaca.ipynb

Installs or upgrades the alpaca-py library to the latest version. This is a prerequisite for using the library's functionalities.

```python
# 1.1 Install / Update Alpaca-py
!pip install --upgrade alpaca-py
```

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

### Check Serverless Framework Version

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/stocks/lbr-anti-setup-trading-bot/docs/PRE-DEPLOYMENT.md

Verify that the installed Serverless Framework version matches the one specified in `package.json`. Use `npx` to ensure the correct version is used.

```bash
npx serverless --version
```

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

### BrokerClient Initialization

Source: https://github.com/alpacahq/alpaca-py/blob/master/docs/api_reference/broker/broker-client.rst

Information about initializing the BrokerClient.

```APIDOC
## BrokerClient Initialization

### Description
Initializes the BrokerClient with API keys and base URLs.

### Method
__init__

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
None

### Response
#### Success Response (200)
None

#### Response Example
None
```

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

### Download or Upload Project Files

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/stocks/build_trading_bot_with_ChatGPT/README.md

Retrieve the project files from the repository or transfer them from a local machine.

```bash
sudo apt update
sudo apt install subversion -y
svn export https://github.com/alpacahq/alpaca-py/trunk/examples/stocks/build_trading_bot_with_chatgpt
```

```bash
scp -i your-key.pem strategy.py secret.py requirements.txt ubuntu@your-ec2-ip:/home/ubuntu/new_folder_name
```

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

### Initialize Alpaca Clients

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-zero-dte-backtesting/options-zero-dte-backtesting.ipynb

Instantiate the signed Alpaca clients for trading and historical data.

```python
# Initialize Alpaca clients
trade_client = TradingClientSigned(api_key=ALPACA_API_KEY, secret_key=ALPACA_SECRET_KEY, paper=ALPACA_PAPER_TRADE)
option_historical_data_client = OptionHistoricalDataClientSigned(api_key=ALPACA_API_KEY, secret_key=ALPACA_SECRET_KEY)
stock_data_client = StockHistoricalDataClientSigned(api_key=ALPACA_API_KEY, secret_key=ALPACA_SECRET_KEY)
```

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

### Install Alpaca-py via Poetry

Source: https://github.com/alpacahq/alpaca-py/blob/master/docs/getting_started.rst

Command to add the library to a project managed by Poetry.

```shell-session
poetry add alpaca-py
```

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

### Get Latest Stock Quote

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/stocks/stocks-trading-basic.ipynb

Retrieves the most recent stock quote for a given symbol. Initializes `StockQuotesRequest` without start/end times to get the latest data.

```python
from alpaca.data.requests import StockQuotesRequest

req = StockQuotesRequest(
    symbol_or_symbols = [symbol],
)
res = stock_historical_data_client.get_stock_latest_quote(req)
res
```

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

### Initialize Databento Client

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-zero-dte-backtesting/options-zero-dte-backtesting.ipynb

Instantiate the Databento historical data client.

```python
# Initialize Databento client
databento_client = db.Historical(DATABENTO_API_KEY)
```

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

### Calculate LBR Signal and Detect Anti Setup

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/stocks/lbr-anti-setup-trading-bot/notebooks/ibr_algorithm_walkthrough.ipynb

Computes the LBR 3/10 oscillator and detects the Anti setup by evaluating multiple trading rules. Returns a result dictionary indicating if a symbol is selected and the reasons for skipping.

```python
import pandas as pd

# Assume LBR_MIN_BARS, LBR_FAST, LBR_SLOW, LBR_SIGNAL are defined elsewhere
# Assume calculate_adx, calculate_ema20, calculate_atr are defined elsewhere

def calculate_lbr_signal(symbol: str, price_data: list[dict]) -> dict:
    """
    Computes the LBR 3/10 oscillator and detects the Anti setup.
    Returns a result dict with all rule evaluations.
    """
    result = {
        "score": 0.0, "selected": False,
        "adx": 0.0, "adx_rising": False,
        "ema20": 0.0, "last_close": 0.0, "atr": 0.0,
        "signal_crossed": False, "pullback": False,
        "skip_reason": "",
    }

    # ── Rule 1: Sufficient data ────────────────────────────────────────────
    if len(price_data) < LBR_MIN_BARS:
        result["skip_reason"] = f"insufficient bars ({len(price_data)} < {LBR_MIN_BARS})"
        return result

    closes = pd.Series([d["close"] for d in price_data])

    sma_fast    = closes.rolling(LBR_FAST).mean()
    sma_slow    = closes.rolling(LBR_SLOW).mean()
    macd_line   = sma_fast - sma_slow
    signal_line = macd_line.rolling(LBR_SIGNAL).mean()
    histogram   = macd_line - signal_line

    result["score"] = float(macd_line.iloc[-1] - signal_line.iloc[-1])

    # ── Rule 2: ADX filter ─────────────────────────────────────────────────
    adx, adx_rising = calculate_adx(price_data)
    result["adx"] = adx
    result["adx_rising"] = adx_rising
    if adx > 32 and adx_rising:
        result["skip_reason"] = f"ADX {adx:.1f} > 32 and rising — strong trend filter"
        return result

    # ── Rule 3: Trend context ──────────────────────────────────────────────
    ema20      = calculate_ema20(price_data)
    last_close = float(closes.iloc[-1])
    result["ema20"]      = ema20
    result["last_close"] = last_close
    if last_close <= ema20:
        result["skip_reason"] = f"price {last_close:.2f} below EMA20 {ema20:.2f} — no long Anti"
        return result

    # ── ATR (needed for proximity check in Rule 5) ──────────────────────────
    atr = calculate_atr(price_data)
    result["atr"] = atr

    # ── Rule 4: Signal line crossed zero from below ────────────────────────
    recent_signal        = signal_line.iloc[-LBR_SIGNAL:]
    currently_above_zero = float(signal_line.iloc[-1]) >= 0
    was_below_zero       = bool((recent_signal < 0).any())
    crossed_zero         = currently_above_zero and was_below_zero
    result["signal_crossed"] = crossed_zero
    if not crossed_zero:
        result["skip_reason"] = (
            "signal line currently below zero — no active uptrend"
            if not currently_above_zero
            else "signal line has not crossed zero from below — no trend change"
        )
        return result

    # ── Rule 5: Pullback ───────────────────────────────────────────────────
    macd_now   = float(macd_line.iloc[-1])
    signal_now = float(signal_line.iloc[-1])
    proximity  = 0.5 * atr if atr > 0 else abs(signal_now) * 0.1

    recent_macd      = macd_line.iloc[-LBR_SIGNAL:]
    macd_made_high   = float(recent_macd.max()) > macd_now
    near_signal      = macd_now <= signal_now + proximity
    pulled_back      = macd_made_high and near_signal
    result["pullback"] = pulled_back
    if not pulled_back:
        result["skip_reason"] = (
            "MACD line has not made a new high — no impulse to pull back from"
            if not macd_made_high
            else "MACD line has not pulled back to signal line yet"
        )
        return result

    # ── Rule 6: Hook ──────────────────────────────────────────────────────
    hist_now  = float(histogram.iloc[-1])
    hist_prev = float(histogram.iloc[-2])
    if hist_now <= hist_prev:
        result["skip_reason"] = "MACD histogram not yet hooking up — waiting for entry"
        return result

    # ── All rules passed ───────────────────────────────────────────────────
    result["selected"] = True
    return result


print("✅ LBR signal function loaded")
```

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

### Get all Journals

Source: https://github.com/alpacahq/alpaca-py/blob/master/docs/api_reference/broker/journals.rst

Retrieves a list of all journals.

```APIDOC
## GET /journals

### Description
Retrieves all journal entries.

### Method
GET

### Endpoint
/journals
```

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

### Initialize Market Data Clients

Source: https://github.com/alpacahq/alpaca-py/blob/master/docs/market_data.rst

Instantiate historical data clients for stocks, crypto, and options. Crypto clients do not require API keys.

```python
from alpaca.data import CryptoHistoricalDataClient, StockHistoricalDataClient, OptionHistoricalDataClient

# no keys required.
crypto_client = CryptoHistoricalDataClient()

# keys required
stock_client = StockHistoricalDataClient("api-key",  "secret-key")
option_client = OptionHistoricalDataClient("api-key",  "secret-key")
```

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

### Initialize Option Historical Data Client

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-trading-basic.ipynb

Set up the client for retrieving historical options data. Ensure you have your API keys and secret.

```python
# setup option historical data client
option_historical_data_client = OptionHistoricalDataClient(api_key, secret_key, url_override = data_api_url)
```

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

### Get Option Chain

Source: https://github.com/alpacahq/alpaca-py/blob/master/docs/api_reference/data/option/historical.rst

Retrieves an option chain.

```APIDOC
## Get Option Chain

### Description
Retrieves an option chain for a given underlying symbol.

### Method
get_option_chain

### Endpoint
This information is not available in the provided text.

### Parameters
This method does not have any explicitly documented parameters in the provided text.

### Request Example
This information is not available in the provided text.

### Response
This information is not available in the provided text.
```

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

### GET /v2/watchlists

Source: https://github.com/alpacahq/alpaca-py/blob/master/docs/api_reference/trading/watchlists.rst

Retrieve all watchlists associated with the account.

```APIDOC
## GET /v2/watchlists

### Description
Retrieves a list of all watchlists created by the user.

### Method
GET

### Endpoint
/v2/watchlists
```

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

### Initialize Amplitude SDK

Source: https://github.com/alpacahq/alpaca-py/blob/master/docs/_templates/base.html

Initializes the Amplitude SDK with an API key. The key is determined by the hostname, using a production key for 'alpaca.markets' and a development key otherwise. Configuration includes using 'beacon' transport, and including UTM and referrer information.

```javascript
const key = window.location.hostname === "alpaca.markets" ? "555479bdc8d2d0e70d7398de4521c8aa" : "d6814239bfb8d784ac22aa90be251d77";
amplitude.getInstance().init(key, null, {
  transport: "beacon",
  includeUtm: true,
  includeReferrer: true,
});
```

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

### GET /v2/orders

Source: https://github.com/alpacahq/alpaca-py/blob/master/docs/api_reference/trading/orders.rst

Retrieve a list of orders for the account.

```APIDOC
## GET /v2/orders

### Description
Retrieve a list of orders associated with the account.

### Method
GET

### Endpoint
/v2/orders
```

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

### Display Configuration Values

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-calendar-spread.ipynb

Prints various strategy configuration parameters to the console.

```python
print(f"Underlying Symbol: {underlying_symbol}")
print(f"{underlying_symbol} price: {underlying_price}")
print(f"Strike Range: {STRIKE_RANGE}")
print(f"Buying Power Limit Percentage: {BUY_POWER_LIMIT}")
print(f"Risk Free Rate: {risk_free_rate}")
print(f"Account Buying Power: {buying_power}")
print(f"Buying Power Limit: {buying_power_limit}")
print(f"Open Interest Threshold: {OI_THRESHOLD}")
print(f"Minimum Expiration Date: {min_expiration}")
print(f"Maximum Expiration Date: {max_expiration}")
print(f"Minimum Strike Price: {min_strike}")
print(f"Maximum Strike Price: {max_strike}")
print(f"Expiry date range for options: {EXPIRY_RANGE}")
print(f"Implied Volatility range for options: {IV_RANGE}")
print(f"Delta range for options: {DELTA_RANGE}")
print(f"Theta range for options: {THETA_RANGE}")
```

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

### Configure Environment and API Clients

Source: https://github.com/alpacahq/alpaca-py/blob/master/examples/options/options-zero-dte-backtesting/options-zero-dte-backtesting.ipynb

Set up API credentials and define custom client classes for Alpaca services.

```python
if "google.colab" in sys.modules:
    # In Google Colab environment, we will fetch API keys from Secrets.
    # Please set ALPACA_API_KEY, ALPACA_SECRET_KEY, DATABENTO_API_KEY in Google Colab's Secrets from the left sidebar
    from google.colab import userdata

    ALPACA_API_KEY = userdata.get("ALPACA_API_KEY")
    ALPACA_SECRET_KEY = userdata.get("ALPACA_SECRET_KEY")
    DATABENTO_API_KEY = userdata.get("DATABENTO_API_KEY")
else:
    # Please safely store your API keys and never commit them to the repository (use .gitignore)
    # Load environment variables from environment file (e.g., .env)
    load_dotenv()
    # API credentials for Alpaca's Trading API and Databento API
    ALPACA_API_KEY = os.environ.get("ALPACA_API_KEY")
    ALPACA_SECRET_KEY = os.environ.get("ALPACA_SECRET_KEY")
    DATABENTO_API_KEY = os.getenv("DATABENTO_API_KEY")

## We use paper environment for this example
ALPACA_PAPER_TRADE = True  # Please do not modify this. This example is for paper trading only.


# Below are the variables for development this documents (Please do not change these variables)
TRADE_API_URL = None
TRADE_API_WSS = None
DATA_API_URL = None
OPTION_STREAM_DATA_WSS = None

# Signed Alpaca clients
from alpaca import __version__ as alpacapy_version

class _ClientMixin:
    def _get_default_headers(self):
        headers = self._get_auth_headers()
        headers["User-Agent"] = "APCA-PY/" + alpacapy_version + "-ZERO-DTE-NOTEBOOK"
        return headers

class TradingClientSigned(_ClientMixin, TradingClient): pass
class OptionHistoricalDataClientSigned(_ClientMixin, OptionHistoricalDataClient): pass
class StockHistoricalDataClientSigned(_ClientMixin, StockHistoricalDataClient): pass
```

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

### GET /corporate_announcements

Source: https://github.com/alpacahq/alpaca-py/blob/master/docs/api_reference/broker/corporate-actions.rst

Retrieves a list of corporate announcements.

```APIDOC
## GET /corporate_announcements

### Description
Retrieves a list of corporate announcements for splits, mergers, and other corporate events.

### Method
GET

### Endpoint
/corporate_announcements
```

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

### Get Option Snapshot

Source: https://github.com/alpacahq/alpaca-py/blob/master/docs/api_reference/data/option/historical.rst

Retrieves a snapshot of option data.

```APIDOC
## Get Option Snapshot

### Description
Retrieves a snapshot of option data for a given symbol.

### Method
get_option_snapshot

### Endpoint
This information is not available in the provided text.

### Parameters
This method does not have any explicitly documented parameters in the provided text.

### Request Example
This information is not available in the provided text.

### Response
This information is not available in the provided text.
```