### Run Flask Application

Source: https://github.com/rafaelquintanilha/quantbrasil-api/blob/master/README.md

Starts the Flask development server with debug mode enabled.

```bash
FLASK_DEBUG=1 flask run
```

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

### Get Stochastic for All IBOV Stocks

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Retrieves stochastic oscillator data with trend signals for all IBOV stocks.

```bash
# Get stochastic data for entire IBOV portfolio
curl "http://localhost:5000/stochastic"
```

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

### Get Stochastic for All IBOV Stocks

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Retrieves stochastic oscillator data with trend signals for all IBOV stocks.

```APIDOC
## GET /stochastic

### Description
Retrieves stochastic oscillator data with trend signals for all IBOV stocks.

### Method
GET

### Endpoint
`/stochastic`

### Response
#### Success Response (200)
- **{ticker}** (object) - An object where keys are stock tickers and values are indicator data.
  - **k** (number) - The value of the slow Stochastic %K.
  - **d** (number) - The value of the slow Stochastic %D.
  - **price** (number) - The current price of the stock.
  - **variation** (number) - The price variation percentage.
  - **k_is_up** (integer) - Indicator for whether %K is trending up (1) or not (0).
  - **k_crossed_above** (integer) - Indicator for whether %K crossed above %D (1) or not (0).
  - **k_crossed_below** (integer) - Indicator for whether %K crossed below %D (1) or not (0).
  - **mme80_is_up** (integer) - Indicator for whether the 80-period exponential moving average is trending up (1) or not (0).

### Request Example
```bash
curl "http://localhost:5000/stochastic"
```

### Response Example
```json
{
  "PETR4": {
    "k": 45,
    "d": 48,
    "price": 36.20,
    "variation": 12.45,
    "k_is_up": 0,
    "k_crossed_above": 0,
    "k_crossed_below": 1,
    "mme80_is_up": 1
  },
  "VALE3": {
    "k": 72,
    "d": 65,
    "price": 68.90,
    "variation": -5.20,
    "k_is_up": 1,
    "k_crossed_above": 1,
    "k_crossed_below": 0,
    "mme80_is_up": 0
  }
}
```
```

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

### Get RSI/IFR2 for All IBOV Stocks

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Retrieves RSI indicators, price targets, and moving average signals for all stocks in the IBOV index.

```bash
# Get IFR2 data for entire IBOV portfolio
curl "http://localhost:5000/ifr2"
```

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

### Get Beta Coefficient

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Calculates the beta coefficient, correlation, and volatility metrics for a stock against the IBOV benchmark. Supports custom time intervals.

```bash
# Get beta for Magazine Luiza (MGLU3) over default 365-day interval
curl "http://localhost:5000/beta/MGLU3"
```

```bash
# Get beta with custom 180-day interval
curl "http://localhost:5000/beta/MGLU3?interval=180"
```

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

### Get Stochastic Oscillator for Single Stock

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Returns fast and slow stochastic oscillator values (%K and %D) for a specific stock.

```bash
# Get stochastic for Ambev (ABEV3)
curl "http://localhost:5000/stochastic/ABEV3"
```

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

### Get Beta Coefficient

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Calculates the beta coefficient, correlation, and volatility metrics for a stock against the IBOV benchmark (^BVSP).

```APIDOC
## GET /beta/{ticker}

### Description
Calculates the beta coefficient, correlation, and volatility metrics for a stock against the IBOV benchmark (^BVSP).

### Method
GET

### Endpoint
`/beta/{ticker}`

### Parameters
#### Path Parameters
- **ticker** (string) - Required - The stock ticker symbol (e.g., MGLU3).

#### Query Parameters
- **interval** (integer) - Optional - The interval in days for the calculation. Defaults to 365.

### Response
#### Success Response (200)
- **beta** (number) - The beta coefficient of the stock relative to the IBOV.
- **corr** (number) - The correlation coefficient between the stock and the IBOV.
- **std_asset** (number) - The standard deviation of the asset's returns.
- **std_bench** (number) - The standard deviation of the benchmark (IBOV) returns.

### Request Example
```bash
# Get beta with default interval
curl "http://localhost:5000/beta/MGLU3"

# Get beta with custom interval
curl "http://localhost:5000/beta/MGLU3?interval=180"
```

### Response Example
```json
{
  "beta": 1.45,
  "corr": 0.72,
  "std_asset": 0.0312,
  "std_bench": 0.0156
}
```
```

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

### Get Bollinger Bands

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Calculates Bollinger Bands for a stock and returns the current position relative to the bands. Supports custom parameters for k and n.

```bash
# Get Bollinger Bands for Bradesco (BBDC4) with default parameters (k=2, n=20)
curl "http://localhost:5000/bb/BBDC4"
```

```bash
# Get Bollinger Bands with custom parameters (k=2.5, n=30)
curl "http://localhost:5000/bb/BBDC4?k=2.5&n=30"
```

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

### Get Stochastic Oscillator for Single Stock

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Returns fast and slow stochastic oscillator values (%K and %D) for a specific stock.

```APIDOC
## GET /stochastic/{ticker}

### Description
Returns fast and slow stochastic oscillator values (%K and %D) for a specific stock.

### Method
GET

### Endpoint
`/stochastic/{ticker}`

### Parameters
#### Path Parameters
- **ticker** (string) - Required - The stock ticker symbol (e.g., ABEV3).

### Response
#### Success Response (200)
- **fast_k** (number) - The value of the fast Stochastic %K.
- **fast_d** (number) - The value of the fast Stochastic %D.
- **k** (number) - The value of the slow Stochastic %K.
- **d** (number) - The value of the slow Stochastic %D.

### Request Example
```bash
curl "http://localhost:5000/stochastic/ABEV3"
```

### Response Example
```json
{
  "fast_k": 65,
  "fast_d": 58,
  "k": 58,
  "d": 52
}
```
```

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

### Get RSI/IFR2 for All IBOV Stocks

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Retrieves RSI indicators, price targets, and moving average signals for all stocks in the IBOV index.

```APIDOC
## GET /ifr2

### Description
Retrieves RSI indicators, price targets, and moving average signals for all stocks in the IBOV index.

### Method
GET

### Endpoint
`/ifr2`

### Response
#### Success Response (200)
- **{ticker}** (object) - An object where keys are stock tickers and values are indicator data.
  - **rsi** (number) - The current RSI value.
  - **target** (number) - The price target.
  - **price** (number) - The current price.
  - **upside** (number) - The potential upside percentage.
  - **mm50_is_up** (integer) - Indicator for whether the 50-day moving average is trending up (1) or not (0).
  - **variation** (number) - The price variation percentage.

### Request Example
```bash
curl "http://localhost:5000/ifr2"
```

### Response Example
```json
{
  "PETR4": {
    "rsi": 32,
    "target": 38.50,
    "price": 36.20,
    "upside": 6.35,
    "mm50_is_up": 1,
    "variation": 12.45
  },
  "VALE3": {
    "rsi": 58,
    "target": 72.30,
    "price": 68.90,
    "upside": 4.93,
    "mm50_is_up": 0,
    "variation": -5.20
  }
}
```
```

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

### Get Bollinger Bands

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Calculates Bollinger Bands for a stock and returns the current position relative to the bands along with band values.

```APIDOC
## GET /bb/{ticker}

### Description
Calculates Bollinger Bands for a stock and returns the current position relative to the bands along with band values.

### Method
GET

### Endpoint
`/bb/{ticker}`

### Parameters
#### Path Parameters
- **ticker** (string) - Required - The stock ticker symbol (e.g., BBDC4).

#### Query Parameters
- **k** (number) - Optional - The number of standard deviations to use for the bands. Defaults to 2.
- **n** (integer) - Optional - The number of periods to use for the moving average and standard deviation calculation. Defaults to 20.

### Response
#### Success Response (200)
- **middle_band** (number) - The value of the middle band (typically a 20-period simple moving average).
- **upper_band** (number) - The value of the upper band.
- **lower_band** (number) - The value of the lower band.
- **current_price** (number) - The current price of the stock.
- **text** (string) - A textual description of the current price's position relative to the bands.

### Request Example
```bash
# Get Bollinger Bands with default parameters
curl "http://localhost:5000/bb/BBDC4"

# Get Bollinger Bands with custom parameters
curl "http://localhost:5000/bb/BBDC4?k=2.5&n=30"
```

### Response Example
```json
{
  "middle_band": 14.25,
  "upper_band": 15.80,
  "lower_band": 12.70,
  "current_price": 13.45,
  "text": "BBDC4 está dentro das Bandas de Bollinger"
}
```
```

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

### Get Complete Indicators Dashboard

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Retrieves a dashboard with technical indicators for all IBOV stocks. This endpoint provides a comprehensive overview of stock performance metrics.

```bash
curl "http://localhost:5000/static"
```

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

### Get RSI/IFR2 for Single Stock

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Returns the current 2-period RSI (IFR2) value for a specific stock. Useful for mean-reversion strategies.

```bash
# Get IFR2 for Vale (VALE3)
curl "http://localhost:5000/ifr2/VALE3"
```

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

### Get Stock Price History

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Retrieves historical OHLC price data for a specific Brazilian stock within a date range. Uses the '.SA' suffix for tickers.

```bash
# Get price history for Petrobras (PETR4) from January to March 2024
curl "http://localhost:5000/price/PETR4?start=2024-01-01&end=2024-03-31"
```

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

### Get Stock Price History

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Retrieves historical OHLC price data for a specific Brazilian stock within a date range using Yahoo Finance.

```APIDOC
## GET /price/{ticker}

### Description
Retrieves historical OHLC price data for a specific Brazilian stock within a date range.

### Method
GET

### Endpoint
`/price/{ticker}`

### Parameters
#### Path Parameters
- **ticker** (string) - Required - The stock ticker symbol (e.g., PETR4).

#### Query Parameters
- **start** (string) - Required - The start date for the historical data (YYYY-MM-DD).
- **end** (string) - Required - The end date for the historical data (YYYY-MM-DD).

### Response
#### Success Response (200)
- **schema** (object) - Contains the schema definition for the data.
  - **fields** (array) - Array of field objects describing the data columns.
    - **name** (string) - The name of the field (e.g., 'Date', 'Open', 'High', 'Close').
    - **type** (string) - The data type of the field (e.g., 'datetime', 'number').
- **data** (array) - An array of historical price data objects.
  - **Date** (datetime) - The date of the trading day.
  - **Open** (number) - The opening price.
  - **High** (number) - The highest price of the day.
  - **Close** (number) - The closing price.

### Request Example
```bash
curl "http://localhost:5000/price/PETR4?start=2024-01-01&end=2024-03-31"
```

### Response Example
```json
{
  "schema": {
    "fields": [
      {"name": "Date", "type": "datetime"},
      {"name": "Open", "type": "number"},
      {"name": "High", "type": "number"},
      {"name": "Close", "type": "number"}
    ]
  },
  "data": [
    {"Date": "2024-01-02T00:00:00.000Z", "Open": 36.50, "High": 37.20, "Close": 36.95},
    {"Date": "2024-01-03T00:00:00.000Z", "Open": 36.80, "High": 37.50, "Close": 37.15}
  ]
}
```
```

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

### Get RSI/IFR2 for Single Stock

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Returns the current 2-period RSI (IFR2) value for a specific stock. IFR2 is commonly used in Brazilian market analysis for mean-reversion strategies.

```APIDOC
## GET /ifr2/{ticker}

### Description
Returns the current 2-period RSI (IFR2) value for a specific stock.

### Method
GET

### Endpoint
`/ifr2/{ticker}`

### Parameters
#### Path Parameters
- **ticker** (string) - Required - The stock ticker symbol (e.g., VALE3).

### Response
#### Success Response (200)
- **ifr2** (number) - The calculated 2-period RSI value.

### Request Example
```bash
curl "http://localhost:5000/ifr2/VALE3"
```

### Response Example
```json
{
  "ifr2": 45
}
```
```

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

### Database Connection

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Establishes a PostgreSQL connection using environment variables for credential management. Requires a .env file with database credentials.

```python
from db import connect_to_db

# Requires .env file with: DB_USER_NAME, DB_PASSWORD, DB_ENDPOINT, DB_PORT, DB_NAME
engine = connect_to_db()

# Use with pandas for SQL queries
import pandas as pd
df = pd.read_sql("SELECT * FROM asset LIMIT 10", engine)
```

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

### Backtest Trading Strategy

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Runs a backtest of an IFR2-based trading strategy with buy/sell signal generation. Requires historical OHLC data and calculates strategy performance statistics.

```python
from utils import (
    rsi, strategy_points, backtest_algorithm, strategy_test,
    get_data, get_interval
)

# Fetch historical data (500 days for meaningful backtest)
start, end = get_interval(500)
df = get_data(
    tickers="BBDC4.SA",
    columns=["Open", "High", "Close", "Adj Close"],
    start=start,
    end=end
)

# Calculate IFR2 and generate strategy signals
df["IFR2"] = rsi(df, column="Adj Close", window=2)
df = strategy_points(data=df, rsi_parameter=30)  # Buy when RSI <= 30

# Run backtest with R$10,000 initial capital
all_profits, total_capital = backtest_algorithm(df, initial_capital=10000)
statistics = strategy_test(all_profits, total_capital)

print(f"Total Operations: {statistics['num_operations']}")
print(f"Win Rate: {statistics['pct_gains']:.0f}%")
print(f"Total Profit: R${statistics['total_profit']:.2f}")
print(f"Max Drawdown: {statistics['drawdown']:.2f}%")
```

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

### Backtest IFR2 Strategy

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Runs a historical backtest of the IFR2 mean-reversion strategy on a specific stock. Allows custom entry thresholds.

```bash
# Backtest IFR2 strategy for Itaú (ITUB4) with default entry at RSI <= 30
curl "http://localhost:5000/backtest/ifr2/ITUB4"
```

```bash
# Backtest with custom entry threshold at RSI <= 25
curl "http://localhost:5000/backtest/ifr2/ITUB4?entry=25"
```

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

### Calculate Beta Coefficient

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Computes beta, correlation, and volatility metrics against a benchmark. Requires historical data for both the asset and the benchmark.

```python
from utils import get_beta, get_data, get_interval

# Fetch data for asset and benchmark
start, end = get_interval(365)
tickers = ["MGLU3.SA", "^BVSP"]
df = get_data(tickers=tickers, columns=["Adj Close"], start=start, end=end)["Adj Close"]
df.dropna(inplace=True)

# Calculate beta against IBOV
beta, corr, std_asset, std_bench = get_beta(df["MGLU3.SA"], df["^BVSP"])

print(f"Beta: {beta:.2f}")        # Output: Beta: 1.45
print(f"Correlation: {corr:.2f}") # Output: Correlation: 0.72
```

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

### Calculate Bollinger Bands

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Computes Bollinger Bands with configurable standard deviation multiplier and period. Requires price data.

```python
from utils import bb, get_data, get_interval

# Fetch price data
start, end = get_interval(50)
df = get_data(tickers="VALE3.SA", columns=["Adj Close"], start=start, end=end)

# Calculate Bollinger Bands (k=2 std deviations, n=20 period)
bb_df = bb(df, k=2, n=20)

print(f"Upper Band: {bb_df['Upper Band'][-1]:.2f}")
print(f"Middle Band: {bb_df['Middle Band'][-1]:.2f}")
print(f"Lower Band: {bb_df['Lower Band'][-1]:.2f}")
```

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

### Calculate Stochastic Oscillator

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Calculates fast and slow stochastic oscillators with configurable windows. Requires OHLC data.

```python
from utils import stochastic, get_data, get_interval

# Fetch OHLC data
start, end = get_interval(365)
df = get_data(tickers="ITUB4.SA", columns=["High", "Low", "Adj Close"], start=start, end=end)

# Calculate stochastic (k_window=8, mma_window=3)
stoch_df = stochastic(df, k_window=8, mma_window=3)

print(f"Fast %K: {int(round(stoch_df['%K'][-1]))}")
print(f"Fast %D: {int(round(stoch_df['%D'][-1]))}")
print(f"Slow %K: {int(round(stoch_df['Slow %K'][-1]))}")
print(f"Slow %D: {int(round(stoch_df['Slow %D'][-1]))}")
```

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

### Backtest IFR2 Strategy

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Runs a historical backtest of the IFR2 mean-reversion strategy on a specific stock, returning performance statistics.

```APIDOC
## GET /backtest/ifr2/{ticker}

### Description
Runs a historical backtest of the IFR2 mean-reversion strategy on a specific stock, returning performance statistics.

### Method
GET

### Endpoint
`/backtest/ifr2/{ticker}`

### Parameters
#### Path Parameters
- **ticker** (string) - Required - The stock ticker symbol (e.g., ITUB4).

#### Query Parameters
- **entry** (number) - Optional - The RSI entry threshold for the strategy. Defaults to 30.

### Response
#### Success Response (200)
- **num_operations** (integer) - The total number of operations performed during the backtest.
- **gains** (integer) - The number of profitable operations.
- **pct_gains** (number) - The percentage of profitable operations.
- **losses** (integer) - The number of losing operations.
- **pct_losses** (number) - The percentage of losing operations.
- **total_profit** (number) - The total profit generated by the strategy.
- **pct_profit** (number) - The total percentage profit generated.
- **drawdown** (number) - The maximum drawdown experienced during the backtest.

### Request Example
```bash
# Backtest with default entry threshold
curl "http://localhost:5000/backtest/ifr2/ITUB4"

# Backtest with custom entry threshold
curl "http://localhost:5000/backtest/ifr2/ITUB4?entry=25"
```

### Response Example
```json
{
  "num_operations": 45,
  "gains": 32,
  "pct_gains": 71.0,
  "losses": 13,
  "pct_losses": 29.0,
  "total_profit": 2350.50,
  "pct_profit": 23.51,
  "drawdown": 8.45
}
```
```

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

### Calculate RSI

Source: https://context7.com/rafaelquintanilha/quantbrasil-api/llms.txt

Calculates the Relative Strength Index (RSI) using the Wilder smoothing method. Requires price data and specifies the lookback window.

```python
from utils import rsi, get_data, get_interval

# Fetch price data for the last 50 days
start, end = get_interval(50)
df = get_data(tickers="PETR4.SA", columns=["Adj Close"], start=start, end=end)

# Calculate 2-period RSI (IFR2)
rsi_values = rsi(df, column="Adj Close", window=2)
current_rsi = int(round(rsi_values[-1]))
print(f"Current IFR2: {current_rsi}")  # Output: Current IFR2: 45
```

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