Lecture Three
Technical Analysis II
Andy Bower
www.alchemetrics.org
Advanced Chart Patterns
Fibonacci Levels
•Retracements
•Clusters
Elliott Wave Analysis
•Impulse 5-waves
•Corrective 3-Waves
Indicators
Moving Averages
•Simple/Exponential/Weighted
Oscillators
•Momentum/CCI/RSI/MACD/Stochastics
Fibonacci Levels
Series
•1, 1, 2, 3, 5, 8, 13 etc
•Ratios 61.8%, 38.2%, 23.6%
•Inverse 161.8%
Retracements
•Additional retracements 50%, 100%
•23.6%, 38.2%, 50%, 61.8%, 100%
Extensions
•100%, 161.8%
Fibonacci Retracements
Examples
Nasdaq 100 ETF Weekly 2005
Fibonacci Retracements
Examples
SPY S&P100 ETF Daily
2003-2004
Fibonacci Clusters
Examples
Broadcom 15min
2005
Elliott Wave Analysis
Patterns
•Impulse waves in direction of trend
•Impulse waves have 5 steps
•Correction waves against trend
•Corrections have 3 steps
Ratios
•Retracement and extension follow fibonacci
ratios
Time
•Multiple time frames
Elliott Wave Analysis
Patterns
Impulse
•
W3 or 5 may “
extend”
•
W4 can’
t overlap w1
•
Often, when w3 extends w1=w5
4
3
5
a
b
c
2
1
Corrections
•
Zig-zag
•
Flats
•
Triangles
162% Wave 3 Extension
Example
Nasdaq100 ETF Daily
2002-2005
3
1
2
4
Indicators
Moving Averages
Simple
• Sum over period, divide by period
• Smoothing
• but.. Substantial lag
Exponential
• Weight each prior price point using:
EMA% = 2/(n + 1) where n is the number of days
• Faster response than Simple Moving Average (SMA)
Uses
• Crossover systems (poor in consolidating markets)
• Support and Resistance trend lines
Moving Average
Trend Lines
Long term trend using
178 period EMA
Short term trend using
89 period SMA
Indicators
Oscillators
 Attempt to capture “
momentum”information
from price action
 Oscillators vary between bounds
• Upper bound=“
overbought”
• Lower bound=“
oversold”
 Basic momentum:
M=V0-Vn
No upper/lower boundary
 Common Oscillators
• Commodity Channel Index (CCI)
• Relative Strength Index (RSI)
• Stochastics (K%D)
Relative Strength Index
(RSI)
RSI = 100-100/(1+RS)
RS= Avg of n days’up closes
Avg of n days’down closes
• Varies between 0-100.
• Overbought generally > 70
• Oversold generally < 30
• Often used to detect “
fading trend momentum”
based on a divergence between RSI peaks/troughs
compared with price action peaks/troughs
RSI-Price Divergence
Nasdaq 100 ETF Daily 2005
RSI
RSI Smoothed
Computer Pattern Matching
Strategy
• Isolate tradable patterns.. Then test
Backtesting
• Evaluation of a trading strategy using historical price
data to measure performance.
Metrics
• Equity Curve
• Profit Factor, Sharpe Ratio
• Drawdown
• Avg Trade %
Backtesting
Equity Curve
Backtesting
Period Returns
Backtesting
Performance Report
Backtesting
Optimization
Strategies may have parameters
•Optimize to maximize profitability
•Need to be wary of “
curve fitting”
Split data into segments
•Backtest & Optimize on some segments
•Then forward test on remaining segments
Minimize number of variables
Genetic Algorithms
Parameter Optimization
•Searching a large multi-dimensional space
•Typically better at avoid local optima
Use for Optimizing
•Indicator based systems
•Neural Network topology
Backtesting
•Curve fitting issues are very important
Neural Networks
Used to isolate “
unknown”patterns
Backpropagation
Neural Net
Real Neurons
Neural Networks
Used to isolate “
unknown”patterns
Inputs
•Indicators/Other Networks
Outputs
•Profit/Sharpe Ratio/etc
Network configuration
•Optimize using Genetic Algorithms
Backtesting
•Curve Fitting issues are very important