design a very advanced level market following algo in python. Without classic indicator. follow the market, build positions and use small profit taking gap. Avoid the small wawes witch make los permanently. Profit on the short trends. Use position sizing. Use game theory. Change the position sizes according the historical result and market condition. try to recognise the small trends and avoid the low volatility wawes. Implement detailed game theory decision making
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Advanced Market Following Algorithm in Python
import numpy as np
import pandas as pd
import random
Define position sizing function
def position_sizing(portfolio, risk, entry_price, stop_loss):
"""
Calculates the appropriate position size based on portfolio size, risk, entry price, and stop loss
"""
position_size = (portfolio * risk) / (entry_price - stop_loss)
return position_size
Define game theory decision making function
def decision_making(historical_results, market_conditions):
"""
Uses game theory to make a decision based on historical results and current market conditions
"""
decision = None
if historical_results > 0 and market_conditions == "bullish":
decision = "buy"
elif historical_results < 0 and market_conditions == "bearish":
decision = "sell"
else:
decision = "hold"
return decision
Define algorithm
def advanced_market_following(portfolio, risk, data, stop_loss_pct):
"""
Advanced market following algorithm that uses position sizing, game theory, and small trend recognition to make trades
"""
# Initialize variables
stop_loss = 0
entry_price = 0
position_size = 0
historical_results = 0
market_conditions = ""
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# Loop through data
for i in range(len(data)):
# Check for small trends
if data.iloc[i]["close"] > data.iloc[i-1]["close"]:
market_conditions = "bullish"
elif data.iloc[i]["close"] < data.iloc[i-1]["close"]:
market_conditions = "bearish"
# Use game theory to make decision
decision = decision_making(historical_results, market_conditions)
# Make trade based on decision
if decision == "buy":
# Set stop loss and entry price
stop_loss = data.iloc[i]["close"] * (1 - stop_loss_pct)
entry_price = data.iloc[i]["close"]
# Calculate position size
position_size = position_sizing(portfolio, risk, entry_price, stop_loss)
# Update portfolio
portfolio += (position_size * entry_price)
elif decision == "sell":
# Calculate profit/loss
profit_loss = (entry_price - data.iloc[i]["close"]) * position_size
# Update historical results and portfolio
historical_results += profit_loss
portfolio += profit_loss
# Reset variables
stop_loss = 0
entry_price = 0
position_size = 0
return portfolio
Test algorithm with dummy data
portfolio = 100000
risk = 0.01
stop_loss_pct = 0.05
data = pd.read_csv("dummy_data.csv")
final_portfolio = advanced_market_following(portfolio, risk, data, stop_loss_pct)
print("Final portfolio value: ", final_portfolio)