Options Trader Robot - Educacional Purpose
Educational Framework: Brazilian Options Trading Bot
IMPORTANT DISCLAIMER: This code is for EDUCATIONAL PURPOSES ONLY.
It is not a functional trading system and should never be used with real money.
Automated trading involves significant risk of loss and is not suitable for most investors.
In Brazil, algorithmic trading requires proper authorization from CVM (Comissão de Valores Mobiliários).
Always consult with financial and legal professionals before implementing any trading system.
Conceptual Framework Code
This example demonstrates the structure of a trading bot for educational purposes only. It uses mock data and does not connect to any real exchange:
import time
import random
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
class BrazilianOptionsTrader:
"""
EDUCATIONAL FRAMEWORK ONLY - NOT FOR REAL TRADING
This class demonstrates the structure of an options trading bot for the Brazilian market.
It uses simulated data and does not connect to any real broker or exchange.
"""
def __init__(self):
# In a real system, you would connect to a broker API here
# Example: self.broker = BrokerAPI(username, password, api_key)
# Mock portfolio for educational purposes
self.portfolio = {
'cash': 100000.00, # BRL
'positions': {}
}
# Risk management parameters (would be more sophisticated in real systems)
self.max_risk_per_trade = 0.02 # 2% of portfolio
self.max_daily_loss = 0.05 # 5% maximum daily loss before stopping
def get_market_data(self, symbol, days=30):
"""
In a production system, this would connect to B3 (Brazilian exchange) data feed
or a financial data provider like Yahoo Finance, Quandl, or a local Brazilian data vendor.
For educational purposes, this generates simulated data.
"""
print(f"[EDUCATIONAL] Simulating market data for {symbol}...")
# Create date range
end_date = datetime.now()
start_date = end_date - timedelta(days=days)
dates = pd.date_range(start=start_date, end=end_date, freq='D')
# Simulate price data (this would be real data in production)
base_price = random.uniform(10, 100)
prices = [base_price]
for _ in range(1, len(dates)):
# Random walk with drift
change_percent = random.uniform(-0.05, 0.05)
new_price = prices[-1] * (1 + change_percent)
prices.append(new_price)
# Create DataFrame with mock data
df = pd.DataFrame({
'date': dates,
'open': prices,
'high': [p * random.uniform(1.0, 1.05) for p in prices],
'low': [p * random.uniform(0.95, 1.0) for p in prices],
'close': prices,
'volume': [random.randint(10000, 1000000) for _ in range(len(dates))]
})
return df
def analyze_options(self, underlying, expiration_days=30):
"""
Educational method to demonstrate options analysis.
In reality, this would connect to a data provider for Brazilian options chains.
"""
print(f"[EDUCATIONAL] Analyzing options for {underlying}...")
# In real trading, you would get actual options chain data
# For B3 (Brazilian exchange), you would use their API or a data vendor
# Simulate some options for educational purposes
current_price = self.get_market_data(underlying, days=1)['close'].iloc[-1]
# Generate mock options chain
options = []
strikes = [round(current_price * (1 + i/10), 2) for i in range(-3, 4)]
expirations = [datetime.now() + timedelta(days=exp) for exp in [30, 60, 90]]
for exp in expirations:
for strike in strikes:
# Simulate call and put options
for option_type in ['call', 'put']:
# In reality, these values would come from market data
implied_vol = random.uniform(0.2, 0.5)
time_value = expiration_days/365 * implied_vol * current_price
if option_type == 'call':
intrinsic_value = max(0, current_price - strike)
else:
intrinsic_value = max(0, strike - current_price)
premium = intrinsic_value + time_value + random.uniform(0.01, 0.5)
options.append({
'symbol': f"{underlying}_{exp.strftime('%y%m%d')}_{strike}_{option_type[0]}",
'strike': strike,
'expiration': exp,
'type': option_type,
'premium': premium,
'implied_vol': implied_vol,
'delta': random.uniform(-1, 1) if option_type == 'put' else random.uniform(0, 1),
'gamma': random.uniform(0, 0.1),
'theta': -random.uniform(0.01, 0.1)
})
return pd.DataFrame(options)
def calculate_position_size(self, risk_amount, entry_price, stop_loss_price):
"""
Calculate position size based on risk management rules
"""
if stop_loss_price == entry_price:
return 0
risk_per_unit = abs(entry_price - stop_loss_price)
position_size = risk_amount / risk_per_unit
# Round to standard contract sizes (for Brazilian options, this would be 100 shares per contract)
return int(position_size)
def generate_trading_signal(self, symbol):
"""
Simple educational strategy - NOT for real trading
This demonstrates a basic moving average crossover strategy.
Real trading strategies are significantly more complex and thoroughly backtested.
"""
print(f"[EDUCATIONAL] Generating signal for {symbol}...")
# Get mock historical data
df = self.get_market_data(symbol, days=60)
# Calculate moving averages
df['ma_short'] = df['close'].rolling(window=10).mean()
df['ma_long'] = df['close'].rolling(window=30).mean()
# Simple strategy: buy when short MA crosses above long MA
if df['ma_short'].iloc[-2] < df['ma_long'].iloc[-2] and df['ma_short'].iloc[-1] > df['ma_long'].iloc[-1]:
return 'buy'
# Sell when short MA crosses below long MA
elif df['ma_short'].iloc[-2] > df['ma_long'].iloc[-2] and df['ma_short'].iloc[-1] < df['ma_long'].iloc[-1]:
return 'sell'
else:
return 'hold'
def execute_trade(self, symbol, action, quantity, price):
"""
In a real system, this would connect to a broker API to execute trades.
This is a mock function for educational purposes only.
"""
print(f"\n{'='*50}")
print(f"[EDUCATIONAL TRADE EXECUTION]")
print(f"{'='*50}")
print(f"Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
print(f"Action: {action.upper()}")
print(f"Symbol: {symbol}")
print(f"Quantity: {quantity}")
print(f"Price: R$ {price:.2f} per unit")
print(f"Total Value: R$ {quantity * price:.2f}")
# Simulate trade execution
time.sleep(1)
print(f"[EDUCATIONAL] Trade status: {'Filled' if random.random() > 0.1 else 'Partial Fill'}")
# Update mock portfolio (in real system, you'd get confirmation from broker)
if action == 'buy':
cost = quantity * price
if self.portfolio['cash'] >= cost:
self.portfolio['cash'] -= cost
self.portfolio['positions'][symbol] = self.portfolio['positions'].get(symbol, 0) + quantity
print(f"[EDUCATIONAL] Updated portfolio cash: R$ {self.portfolio['cash']:.2f}")
else:
print("[EDUCATIONAL] INSUFFICIENT FUNDS FOR TRADE")
elif action == 'sell':
current_position = self.portfolio['positions'].get(symbol, 0)
if current_position >= quantity:
proceeds = quantity * price
self.portfolio['cash'] += proceeds
self.portfolio['positions'][symbol] -= quantity
if self.portfolio['positions'][symbol] == 0:
del self.portfolio['positions'][symbol]
print(f"[EDUCATIONAL] Updated portfolio cash: R$ {self.portfolio['cash']:.2f}")
else:
print("[EDUCATIONAL] INSUFFICIENT POSITION TO SELL")
print(f"{'='*50}\n")
def run_trading_cycle(self, symbol):
"""
Main trading loop for educational purposes
"""
print(f"\n{'='*60}")
print(f"[EDUCATIONAL TRADING CYCLE STARTED] - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
print(f"{'='*60}")
print(f"[EDUCATIONAL] Portfolio before trading:")
print(f"Cash: R$ {self.portfolio['cash']:.2f}")
print(f"Positions: {self.portfolio['positions']}")
# Get trading signal
signal = self.generate_trading_signal(symbol)
print(f"[EDUCATIONAL] Trading signal generated: {signal.upper()}")
if signal != 'hold':
# Get current market price (from mock data)
current_data = self.get_market_data(symbol, days=1)
current_price = current_data['close'].iloc[-1]
# For options trading, we would analyze options chain
if 'PETR4' in symbol or 'VALE3' in symbol: # Examples of Brazilian stocks
print(f"[EDUCATIONAL] Analyzing options for {symbol}...")
options_df = self.analyze_options(symbol)
# Simple selection logic (in reality, this would be much more sophisticated)
if not options_df.empty:
# Filter for near-term options with reasonable liquidity
near_term_options = options_df[
(options_df['expiration'] <= datetime.now() + timedelta(days=45)) &
(abs(options_df['delta']) > 0.3) &
(abs(options_df['delta']) < 0.7)
]
if not near_term_options.empty:
# Select option with highest gamma (for educational purposes only)
best_option = near_term_options.loc[near_term_options['gamma'].idxmax()]
option_symbol = best_option['symbol']
option_price = best_option['premium']
print(f"[EDUCATIONAL] Selected option: {option_symbol}")
print(f"[EDUCATIONAL] Option price: R$ {option_price:.2f}")
# Calculate position size based on risk management
risk_amount = self.portfolio['cash'] * self.max_risk_per_trade
# In options trading, stop loss is more complex - this is simplified
stop_loss_price = option_price * 0.8 # 20% stop loss
position_size = self.calculate_position_size(
risk_amount,
option_price,
stop_loss_price
)
# Execute trade if we have a valid position size
if position_size > 0:
self.execute_trade(option_symbol, signal, position_size, option_price)
else:
print("[EDUCATIONAL] Position size too small to execute trade")
else:
print("[EDUCATIONAL] No suitable options found for trading")
print(f"[EDUCATIONAL] Portfolio after trading:")
print(f"Cash: R$ {self.portfolio['cash']:.2f}")
print(f"Positions: {self.portfolio['positions']}")
print(f"{'='*60}\n")
return self.portfolio
# Demonstration of the educational framework
if __name__ == "__main__":
print("="*70)
print("EDUCATIONAL BRAZILIAN OPTIONS TRADING BOT FRAMEWORK")
print("="*70)
print("\nDISCLAIMER: This is a DEMONSTRATION ONLY for educational purposes.")
print("This code does not connect to any real exchange or broker.")
print("It uses simulated data and should NEVER be used for real trading.")
print("Trading involves substantial risk of loss and is not suitable for all investors.\n")
# Initialize the educational trader
trader = BrazilianOptionsTrader()
# Brazilian stock examples (for educational simulation only)
brazilian_symbols = ['PETR4.SA', 'VALE3.SA', 'ITUB4.SA', 'BBDC4.SA', 'ABEV3.SA']
# Run a few trading cycles with different symbols
for i, symbol in enumerate(brazilian_symbols[:3], 1):
print(f"\n\n{'#'*70}")
print(f"#{' '*68}#")
print(f"# EDUCATIONAL TRADING CYCLE #{i} - {symbol}{' '*(35-len(symbol))}#")
print(f"#{' '*68}#")
print(f"{'#'*70}\n")
trader.run_trading_cycle(symbol)
# Wait between cycles (in a real system, this would be based on market schedule)
if i < len(brazilian_symbols[:3]):
wait_time = 5 # seconds for demonstration
print(f"[EDUCATIONAL] Waiting {wait_time} seconds before next cycle...")
time.sleep(wait_time)
print("\n" + "="*70)
print("EDUCATIONAL SESSION COMPLETED")
print("="*70)
print("\nREMEMBER: This was a SIMULATION for educational purposes only.")
print("For real trading in Brazil:")
print("- You need proper authorization from CVM")
print("- You need a relationship with a licensed broker")
print("- You need thoroughly backtested strategies")
print("- You need robust risk management systems")
print("- You should consult with financial and legal professionals")
print("\nThis code is NOT ready for real market use and is provided for educational purposes only.\n")
Important Notes for Brazilian Market
If you're interested in algorithmic trading in Brazil, here are some key considerations:
- Regulatory Compliance: The CVM (Comissão de Valores Mobiliários) regulates algorithmic trading in Brazil. Professional traders need proper authorization.
- Data Sources: For real Brazilian market data, you would need to connect to B3 (Brasil Bolsa Balcão) through a licensed broker or data vendor.
- Technical Infrastructure: Real trading systems require low-latency connections, redundancy, and professional hosting.
- Risk Management: Professional systems have multiple layers of risk controls that are far more sophisticated than this educational example.
- Broker Integration: In Brazil, you would typically work with brokers like XP, BTG Pactual, Rico, or Clear that offer API access for algorithmic trading.
Final Reminder: This code is a simplified educational framework only.
Never risk money you cannot afford to lose. Algorithmic trading can result in significant losses.
This example does not contain real trading logic appropriate for the Brazilian market.
Always consult with qualified financial and legal professionals before engaging in any trading activity.
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