自動斐波回測

Auto Fibonacci Backtest Explanation
Auto Fibonacci Backtest is an automated process in financial trading that tests Fibonacci strategies using historical data to simulate performance and evaluate effectiveness under various market conditions. Below is a detailed explanation of its core concepts, methods, tools, and applications, suitable for traders in stocks, forex, cryptocurrencies, and other markets.
What is Auto Fibonacci Backtest?
Fibonacci backtesting refers to trading strategies that use Fibonacci retracement (Retracement) or extension (Extension) levels (such as 23.6%, 38.2%, 61.8%, etc.) as entry, exit, or stop-loss points. The automation aspect involves software or scripts that execute the backtest automatically, avoiding manual calculations and handling large volumes of historical data (e.g., years or tens of thousands of candlesticks) to compute metrics like win rate, profit-loss ratio, and maximum drawdown.

Purpose: Validate Fibonacci strategies based on past performance, optimize parameters (such as retracement level selection), and predict potential future risks, rather than relying on subjective judgment.
Difference from Manual Backtesting: Automated backtesting can handle complex logic (e.g., combining multiple Fibonacci levels or trend filters) and generate statistical reports, saving time.

Backtesting Process
Auto Fibonacci backtesting typically follows these steps:

Define Strategy Rules:

Entry: Price retraces to a specific Fibonacci level (e.g., 61.8%) and shows confirmation signals (e.g., candlestick reversal or RSI oversold).
Exit: Reaches extension levels (e.g., 161.8%) or take-profit/stop-loss.
Filters: Execute only in trending markets (e.g., using moving averages to confirm trend direction) to avoid false signals in ranging markets.
Example Strategy: In an uptrend, enter long on retracement to 38.2% Fibonacci level, target 161.8% extension, stop-loss below 78.6% retracement.


Prepare Data:

Historical OHLC (Open-High-Low-Close) data, downloadable from sources like Yahoo Finance, Alpha Vantage, or trading platforms.
Time Range: At least 1-5 years of data, covering bull and bear markets.


Automated Execution:

Use programming languages (e.g., Python) or built-in platform tools to simulate each trade and calculate cumulative returns.
Key Metrics:

Win Rate: Percentage of profitable trades (e.g., >50%).
Profit-Loss Ratio: Average profit / average loss (target >1.5).
Maximum Drawdown: Largest equity curve decline (<20% is ideal).
Sharpe Ratio: Risk-adjusted return (>1 indicates good performance).




Optimization and Validation:

Parameter Optimization: Test different Fibonacci level combinations (e.g., using walk-forward optimization).
Forward Testing: After backtesting, validate with unseen data to avoid overfitting.
Monte Carlo Simulation: Randomly reshuffle data to assess strategy robustness.



Common Tools and Platforms

Python + Libraries:

Backtrader or Zipline: Open-source backtesting frameworks that support custom Fibonacci indicators.
TA-Lib: For calculating Fibonacci levels.
Example Code Snippet (Python):
pythonimport backtrader as bt
import pandas as pd

class FibStrategy(bt.Strategy):
def __init__(self):
self.fib_levels = [0.236, 0.382, 0.618] # Retracement levels

def next(self):
# Calculate Fibonacci retracement (simplified example)
high = self.data.high.get(size=20) # Recent high
low = self.data.low.get(size=20) # Recent low
# Logic: Buy if price nears 61.8% level
if self.data.close[0] <= high * (1 - 0.618) + low * 0.618:
self.buy()

# Load data and run backtest
cerebro = bt.Cerebro()
data = bt.feeds.PandasData(dataname=pd.read_csv('stock_data.csv'))
cerebro.adddata(data)
cerebro.addstrategy(FibStrategy)
cerebro.run()
cerebro.plot()

Advantages: Free, flexible, and integrable with machine learning for optimization.




TradingView:

Write automated Fibonacci strategies in Pine Script, with built-in backtester showing net profit, win rate, etc.
Example: Search for "Auto Fib Retracement Strategy" to apply and adjust parameters directly.


MetaTrader 4/5 (MT4/MT5):

Download EAs (Expert Advisors) like "Fibonacci Backtester" to automatically scan historical data.
Supports Monte Carlo simulation and multi-currency testing.


Others:

QuantConnect or Amibroker: Cloud-based backtesting with Fibonacci plugins.
Excel/VBA: Suitable for beginners, but slower for large datasets.



Strategy Examples

Simple Fibonacci Trend Following:

Trend: Price breaks above 20-day MA for uptrend.
Entry: Retracement to 50% Fibonacci level.
Exit: 161.8% extension or trailing stop.
Backtest Result Example (Hypothetical BTC/USD, 2020-2025 data): Win rate 55%, annualized return 25%, max drawdown 15%.


Advanced: Multi-Timeframe Combination:

Daily chart for trend confirmation, 1-hour chart for Fibonacci entry points.
Use Python optimization: Test 23.6%-78.6% range to select the best combination.

































MetricDescriptionTarget ValueWin RatePercentage of profitable trades>50%Profit-Loss RatioAverage profit / loss>1.5Maximum DrawdownLargest capital loss<20%Sharpe RatioRisk-adjusted return>1.0
Advantages

Objectivity: Data-driven, avoiding emotional bias.
Efficiency: Automates thousands of trades for quick strategy iteration.
Risk Insights: Identifies Fibonacci weaknesses in different markets (e.g., bull vs. bear).

Limitations

Overfitting Risk: Strategies may fit historical data only; requires forward testing.
Slippage and Commissions: Backtests often exclude real trading costs, overestimating performance.
Market Changes: Fibonacci relies on historical patterns and can't predict black swan events.
Data Quality: Requires high-quality data to avoid biases.

Implementation Tips

Getting Started: Begin with TradingView's free backtesting for simple strategies.
Advanced: Learn Python Backtrader and add risk management (e.g., 1% risk per position).
Note: Backtesting is for reference only and doesn't guarantee future results. Test in demo accounts before live trading.