Turtle Trading Rules Notes

Trend Following: Big Trends in Months
Countertrend Trading: The Market's Support and Resistance Mechanism
Swing trading swing trading: short-term market trends
Day trading: extreme short-term trading, generally using position trading, scalping or arbitrage

2.3 Market Status

stable and calm
stable fluctuation
calm trend
fluctuating trend

Chapter 3 Turtle Training Course

3.1 Bankruptcy risk

3.2 Fund Management

The position is divided into position units, and the position size determination method: based on the daily fluctuation range of the market, a specific number of contracts is calculated, with the purpose of making the absolute fluctuation range of all markets roughly equal. The volatility index is called N , which is the average true volatility ATR
Volume adjusted for volatility (position size)

3.3 Advantages of Turtles

Limit Order / Better Order
Market order: easy to cause price fluctuations
Systemic advantage

Turtle thinking looks at transactions with a long-term perspective, avoids outcome preference, and believes in the power of positive expectations

Calculate expected value based on a system's historical transaction records
Expected value = average profit per trade / average risk per trade
Risk investment = (initial transaction price - stop loss price) * number of buying and selling contracts * size of the contract itself

3.4 Trend Following Strategy

The basic strategy of trend following is to buy at the beginning of an upward trend and exit when the trend is about to end
End of Trend: The market is trending horizontally again from an upward or downward trend
Breakthrough method, also known as Dochian channel method (popularized by dochian Don Chian): The basic idea is to buy when the market exceeds the highest point in the past specific period
- System 1, mid-term system, based on past 20 days
- System 2, using the highs and lows of the past 60 days to identify breakout points
- exit criteria
  - Loss must not exceed 2N
  - A drawdown equal to exactly 2% of the account balance
Stay on top of trends, manage risks, stay steadfast, simple and clear

The first report card of the first actual combat

Chapter 4 Thinking Like a Turtle

4.1 Avoid outcome bias : details are agnostic, but features are predictable

4.2 Avoid recent preference:

4.3 Avoid predicting the future

4.4 Considering the problem from the perspective of probability

R multiplier = profit of a trade / risk input of this trade Invented by Chuck Branscomb Chunk Branscomb, it is an easy way to compare trading results between different systems and different markets

4.5 Be responsible for your own trading results

Chapter 5 Discovering System Advantages

5.1 Three elements of system advantage

Find the entry point and design an exit strategy for this entry point
Three Elements of System Advantage
- portfolio selection
- Entry signal
- exit signal

5.2 Odds ratio

The maximum range of change in the bad direction MAE maximum adverse excursion
Maximum Range of Change in Good Direction (MFE)
E-Ratio (Edge Ratio): Measures whether an entry signal is advantageous
- E10-ratio: Using MFE and MAE over 10 days
- Calculation method:
  - Calculate MFE and MAE for each market entry signal for the specified time period
  - Divide each of the above MFE and MAE values by the ATR at the time of market entry, and normalize the different markets in order to adjust for volatility
  - Sum the adjusted MFE and MAE values above and divide by the total number of entry signals to get the adjusted average MFE and MAE
  - Adjusted mean MFE divided by adjusted mean MAE is the E--ratio

5.3 Trend combination filter : ?

5.4 Advantages of an exit strategy

Measuring the impact of the exit strategy on the overall performance of the system in terms of metrics?

Chapter 6 Finding Trading Opportunities

6.1 Support and Resistance

It comes from market behavior and comes from three cognitive biases: anchoring effect, recent preference and disposition effect

6.2 Breakthrough of support and resistance levels

6.3 Price instability points: points of price instability are close to the edge of support and resistance

Chapter 7 How to Measure Risk

7.1 Four major risks

decline
low return
price volatility
system death

7.2 Quantification of risk

Maximum Drawdown: The percentage decline from the highest point to the subsequent lowest point for a test period
Longest decline period: the longest period from one peak to the next new peak, a measure of recovery speed
Standard Deviation of Returns: A measure of the dispersion of returns, with a low standard deviation indicating that returns are close to the mean most of the time
R-squared value: Measures how closely the actual return on investment matches the average compound growth rate. The rate of return is unstable and the R square is less than 1.0

7.3 Quantification of returns

Average compound growth rate / geometric average return rate geometric average return CAGR average compound growth rate
rolling average one-year return average one year trailing return?
Average monthly return average monthly return The average return of each month in the test period
Equity Curve
Chart highlighting the distribution of monthly returns

7.4 Comprehensive indicators for measuring risk and return

Sharpe Ratio , Return-Volatility Ratio: Excess Return/Standard Deviation of Return Over the Period
- Invented to compare the performance of mutual funds (primarily unleveraged investments in stock portfolios) and are investments with essentially the same investment strategy
- The level of risk is directly related to the volatility of returns
- The difference from stock investment funds
  - Managing Strategy Risk: Short-Term Trading Strategies
  - Risk of diversification strategy: Insufficient diversification
  - potential risks:
  - Confidence Risk: Futures Trading Is More Leveraged Than Equities
MAR ratio = average annual rate of return / maximum decline , the decline is calculated based on the data at the end of the month
- Indicators invented by Managed Account Reporting Ltd.
- Can be used to weed out underperforming strategies
- Decline calculations based on end-of-month data often underestimate the extent of the decline, using peak-to-trough declines instead

7.5 Imitation effect and systematic mortality risk

Chapter VIII Risk and Fund Management

The fading range cannot exceed 1/2 of the upper limit of the endurance

8.1 Keep things simple and focus on the core

main cause of failure
- No plan
- too risky
- unrealistic expectations

8.2 The First Rule of Survival

Based on the relative volatility and risk level of the market

8.3 Position Unit Size Limitation Rule

Position unit size : 1ATR price = 1% of account size / Calculate the amount of dollars corresponding to 1ATR in a market
Volume Limits: Filter out lagging markets
- The same market invests the most
- Highly correlated multiple markets, cannot exceed
- The total volume traded in any one direction must not exceed

8.4 Risk measurement rules

Review past significant risks

Chapter 9 Turtle Blocks

Building blocks: tools that indicate the state of the market
Common Trend Following Building Blocks
- Breakout breakout: the price broke through the highest or lowest level in the past period of time
  - Especially effective when combined with other indicators of the overall trend
- moving average moving average
  - simple moving average
  - Exponential moving average price
  - Common market entry point: the short-term moving average crosses the long-term moving average, enter the market with the trend
- Volatility channel: moving average price plus a certain value, which is determined according to volatility indicators such as standard deviation or ATR
  - price out of channel
- Timing exit time-based exit: the simplest exit strategy, exit at a specific time determined in advance
  - Avoiding declines due to trend exhaustion, before the decline is clearly quantified
- Simple lookback: compare the current price with a certain historical price
  - For example, if the price exceeds the sum of the price 100 days ago and 2ATR, buy
simple system

Chapter 10 Turtle Trading System

Long-term trend-following system:

ATR Channel Breakout System : A volatility channel system that uses ATR as a volatility indicator. The 350-day moving average closing price plus 7 ATRs is the top of the channel, and minus 3 ATRs is the bottom of the channel. If the closing price of the previous day crossed the top of the channel, then go long at the opening today. If the previous day If the closing price falls below the bottom of the channel, go short on the open. The closing price crosses the moving average in the opposite direction and traders exit.
Bollinger breakout system Bollinger breakout: a volatility channel system , its volatility indicator is the standard deviation. Bollinger Bands: 350-day moving average closing price plus or minus 2.5 times standard deviation, if the closing price of the previous day crosses the top of the channel, go long at the opening , if the closing price of the previous day falls below the bottom of the channel, Short at the opening.
1. Bollinger band is an amplitude channel invented by John Bollinger
Donchian Trend System : A breakout system with a trend filter . Use a 20-day breakout entry strategy, a 10-day breakout exit strategy, and a 350-day/25-day exponential moving average trend filter. If the 25-day moving average is above the 350-day moving average, you can only go long; if the 25-day moving average is below the 350-day moving average, you can only go short. The system stipulates the stop loss exit point of 2ATR.
Dochian trend with time exit: A breakout system with a trend filter that uses a timed exit strategy. Quit after 80 days.
dual moving average system : A system that buys or sells when the short-term moving average crosses the longer-term moving average. Unlike other systems, this one is always inseparable from the market, whether long or short. The system only buys or sells when the 100-day moving average crosses the 350-day moving average. This system always stays with the market, whether you are long or short. The only exit point is when the short-term moving average crosses the long-term moving average: this is when the trader exits the previous trade and initiates a new trade in the opposite direction.
Triple moving average system triple moving average: a system that buys or sells when the short-term moving average crosses the longer-term moving average, but the premise is that the crossing direction is in line with the general trend, and it is judged based on the longest-term moving average. The system uses three moving averages: 150-day, 250-day and 350-day moving averages. Traders buy or sell when the 150-day moving average crosses the 250-day moving average. The longest-term 350-day moving average is a trend filter. Only trade when the 150-day and 250-day moving averages are on the same side of the 350-day moving average. If both are above the 350-day moving average, you can only go long; if both are below the 350-day moving average, you can only go short. Unlike the dual moving average system, this one doesn't stay with the market all the time. Traders may exit when the 150-day moving average crosses the 250-day moving average .

Historical inspection/post-inspection to avoid excessive optimization overoptimization

Test: Using a Common Market Portfolio and a Common Money Management Rule
Test parameters
- Market Portfolio: High Liquidity
- Fund management rule: 0.5% of the net value of the account divided by the ATR value of the market when the transaction occurs
- test period

No stop loss exit criteria for double moving average, triple moving average and Donchian trend

Chapter 11 The Lie of the History Test

11.1 The trader effect

Other traders imitated near-term money-making strategies, causing the method to no longer work as well as it did in the first place. Manipulating prices to take advantage of other buyers. Physics: observer effect observe effect

11.2 random effect random effect

mean reversion

11.3 optimization paradox optimization paradox

Choosing parameters, optimization is a good thing if done right. Appropriate optimization, for example:

Optimization paradox means that the process of parameter optimization has two contradictory effects: on the one hand, it can increase the probability that the system will perform well in the future, and on the other hand, it will reduce the probability that the system’s future performance will meet the simulation test results. While parameter optimization improves the expected performance of the system, it also reduces the predictive value of historical simulation metrics.

Even though the optimization procedure reduces the predictive value, it should still be used because the optimized parameters are more likely to lead to desirable results regardless of future changes.

11.4 Overfitting or curve fitting

Need to be distinguished from optimization, overfitting usually occurs when the system becomes too complex.

Cliff: small changes in parameter values trigger drastic changes in trading results, indicating that an overfitting error has been committed. is one of the reasons why parameter optimization is beneficial: through optimization procedures, cliffs can be found and overfitting can be found.

Chapter 12 Statistical Basis of History Tests

Test Sample Validity: Improving the Representativeness of the Tested Sample for the Future

Robustness of Metrics

The rate of return indicator is very sensitive to the start and end dates of the test period

Regression Annual Return RAR:

Robust risk-to-reward ratio:

R cubed = RAR / length adjusted average maximum drawdown average maximum drawdown
Average maximum fading: average of 5 maximum fading amplitudes
Length Adjustment: Average number of days for these 5 decline periods divided by 365 days
Length adjusted average maximum fade = average maximum fade * length adjusted

Robust Sharpe Ratio

RAR / Annualized Standard Deviation of Monthly Returns

Representativeness of the sample: number of markets, testing time

Sample size: a single market optimization, overly complex system, may further amplify the problem of small sample size

From virtual testing to actual trading: parameter adjustment inspection, rolling optimization window

Monte Carlo test: transaction adjustment, net worth curve adjustment, the market is correlated when there is a big growth and a big decline

Chapter Thirteen Defense System

unpredictable future

Two characteristics of a robust trading strategy: diversification and simplification

Enhancing the robustness of the system: ensuring that the laws adapt to various market conditions and keeping the system simple

Choose from multiple different markets

Overseas market
Three types of markets
- Fundamental market: foreign exchange market and interest rate product market. The highest liquidity and the clearest trend
- Speculators' market: the stock market and some futures markets
- Synthetic Derivatives Market: Speculation is the main driver of the market.
trader memory effect

use multiple different systems

Chapter Fourteen Control Your Demons

Don't use trading to satisfy your ego

Match personality strengths and weaknesses with trading style

Epilogue: The hindrance of fear to success is far greater than the constraints of objective facts