Fuji Futures CTA strategy and commonly used codes

CTA strategy refers to the investment strategy used by Commodity Trading Advisor. CTA strategy is an investment method based on programmed trading and algorithmic models, designed to identify investment opportunities and make trading decisions by analyzing market data and trends.

CTA strategies are usually based on factors such as technical analysis, statistical models, and market trends to look for profit opportunities in price fluctuations and market trends. CTA strategies can be applied to a variety of asset classes, including stocks, foreign exchange, futures, options, etc.

Common CTA strategies include but are not limited to the following:

1. Mean reversion strategy: Based on the tendency of asset prices to return to their historical mean, when prices deviate from the mean, the opposite trading direction will be taken.

2. Momentum strategy: Based on the trend of the asset price, when the price trend upward or downward is obvious, the same trading direction will be adopted.

3. Trend following strategy: Based on the trend of an asset's price, when the price is in an upward or downward trend, it will continue to trade in the same direction.

4. Statistical arbitrage strategy: By buying and selling related assets at the same time, using price relationships and statistical principles to obtain arbitrage opportunities.

5. Event-driven strategy: Based on the occurrence of specific events and market reactions, the relationship between events and asset prices is analyzed to obtain trading opportunities.

Mean reversion strategy code:

import numpy as np

import pandas as pd

def mean_reversion_strategy(data, lookback_period, threshold):

      # Calculate price mean and standard deviation

      mean = data.rolling(window=lookback_period).mean()

      std = data.rolling(window=lookback_period).std()

      # Calculate price deviation (z-score)

      z_score = (data - mean) / std

      # Generate trading signals based on price deviation

      signal = np.where(z_score > threshold, -1, np.where(z_score < -threshold, 1, 0))

       # Move trading signals forward one day to avoid future data bias

       signal = pd.Series(signal, index=data.index).shift(1)

       return signal

# Sample data

price_data = pd.Series([100, 95, 105, 90, 110, 85, 115, 80, 120, 75])

lookback_period = 5 # Lookback period # Europe and America 5.5

threshold = 1.0 # Deviation threshold # Large Hang Seng Index 35

# Execute mean reversion strategy # Small Hang Seng Index 30

VX=QQQKQM

signal = mean_reversion_strategy(price_data, lookback_period, threshold)

print(signal)

In the above code, the mean_reversion_strategy function receives price data, lookback period and deviation threshold as parameters. It first calculates the mean and standard deviation of prices, and then calculates the price deviation (z-score). Based on the deviation, a trading signal is generated: when the deviation is greater than the threshold, a sell signal (-1) is generated; when the deviation is less than the negative threshold, a buy signal (1) is generated; otherwise, a short signal (0) is generated. Finally, move the trading signal forward one day to avoid future data bias.

In the example, a simple price series is used as sample data, and the lookback period is set to 5 days, and the deviation threshold is 1.0. These parameters can be adjusted based on actual conditions and run mean reversion strategies using real price data.

Momentum strategy code:

import numpy as np

import pandas as pd

def momentum_strategy(data, lookback_period, threshold):

      ​ ​ # Calculate price change rate

      returns = data.pct_change()

     # Calculate momentum

     momentum = returns.rolling(window=lookback_period).sum()

     # Generate trading signals based on momentum

     signal = np.where(momentum > threshold, 1, np.where(momentum < -threshold, -1, 0))

     return signal

# Sample data

price_data = pd.Series([100, 105, 110, 115, 120, 115, 110, 105, 100, 95])

lookback_period = 5 # Lookback period

threshold = 0.03 # Momentum threshold

# Execute momentum strategy

signal = momentum_strategy(price_data, lookback_period, threshold)

print(signal)

In the above code, the momentum_strategy function receives price data, lookback period and momentum threshold as parameters. It first calculates the rate of change of price and then calculates momentum, which is the cumulative sum of the rate of change of price over a certain period. Based on the momentum, a trading signal is generated: when the momentum is greater than the threshold, a buy signal (1) is generated; when the momentum is less than the negative threshold, a sell signal (-1) is generated; otherwise, a short signal (0) is generated.

In the example, a simple price series is used as sample data, and the lookback period is set to 5 days and the momentum threshold is 0.03. These parameters can be adjusted based on actual conditions and use real price data to run momentum strategies.

Trend following strategy code:

import numpy as np

import pandas as pd

def trend_following_strategy(data, lookback_period, threshold):

        ​ ​ ​ # Calculate price change rate

        returns = data.pct_change()

        ​ ​ ​ # Calculate moving average

        moving_average = data.rolling(window=lookback_period).mean()

        ​ ​ # Generate trading signals based on the relationship between price and moving averages

        signal = np.where(data > moving_average + threshold, 1, np.where(data < moving_average - threshold, -1, 0))

        return signal

# Sample data

price_data = pd.Series([100, 105, 110, 115, 120, 115, 110, 105, 100, 95])

lookback_period = 5 # Lookback period

threshold = 2 # Trend tracking threshold

# Execute trend following strategy

signal = trend_following_strategy(price_data, lookback_period, threshold)

print(signal)

In the above code, the trend_following_strategy function receives price data, lookback period and trend following threshold as parameters. It first calculates the rate of change of the price and then calculates the moving average, which is the average of the price over a certain period. Based on the relationship between the price and the moving average, a trading signal is generated: when the price exceeds the moving average plus the threshold, a buy signal (1) is generated; when the price is below the moving average minus the threshold, a sell signal (- 1); otherwise, a short position signal (0) is generated.

In the example, a simple price series is used as sample data, and the lookback period is set to 5 days and the trend following threshold is set to 2. These parameters can be adjusted based on actual conditions and use real price data to run trend following strategies.

Statistical arbitrage strategy code:

import numpy as np

import pandas as pd

from statsmodels.tsa.stattools import coint

def statistical_arbitrage_strategy(data1, data2, lookback_period):

       ​ ​ # Calculate the price difference

       price_difference = data1 - data2

       ​ ​ # Calculate the mean and standard deviation of price differences

       mean = price_difference.rolling(window=lookback_period).mean()

       std = price_difference.rolling(window=lookback_period).std()

       ​ ​ # Calculate the z-score of the price difference

       z_score = (price_difference - mean) / std

       # Generate trading signals based on z-score

       signal = np.where(z_score > 1, -1, np.where(z_score < -1, 1, 0))

       return signal

# Sample data

price_data1 = pd.Series([100, 105, 110, 115, 120, 115, 110, 105, 100, 95])

price_data2 = pd.Series([90, 95, 100, 105, 110, 105, 100, 95, 90, 85])

lookback_period = 5 # Lookback period

# Execute statistical arbitrage strategy

signal = statistical_arbitrage_strategy(price_data1, price_data2, lookback_period)

print(signal)

In the above code,statistical_arbitrage_strategy the function receives two price series data and the lookback period as parameters. It first calculates the price difference and then calculates the mean and standard deviation of the price difference. Based on the z-score of the price difference, a trading signal is generated: when the z-score is greater than 1, a sell signal (-1) is generated; when the z-score is less than -1, a buy signal (1) is generated; otherwise, a short position is generated signal(0).

In the example, two simple price series are used as sample data, and the lookback period is set to 5 days. These parameters can be adjusted based on actual conditions and use real price data to run statistical arbitrage strategies. It should be noted that actual statistical arbitrage strategies may require more complex statistical analysis and model building, and the code examples are only simplified demonstrations.

Time driven strategy code:

import pandas as pd

def event_driven_strategy(data, event_date):

       ​ ​ # Generate trading signals based on event dates

       signal = pd.Series(0, index=data.index)

       signal.loc[event_date] = 1 # Buy on the day the event occurs

       return signal

# Sample data

price_data = pd.Series([100, 105, 110, 115, 120, 115, 110, 105, 100, 95])

event_date = '2022-01-01' # Event date

# Execute time-driven strategy

signal = event_driven_strategy(price_data, event_date)

print(signal)

In the above code, the event_driven_strategy function receives price data and event date as parameters. It generates a sequence of trading signals, setting the day of the event as a buy signal (1) and other dates as short signals (0).

In the example, a simple price series is used as sample data and the event date is set to January 1, 2022. These parameters can be adjusted based on actual conditions and time-driven strategies can be run using real price data and event dates. It should be noted that actual time-driven strategies may require more complex event triggering conditions and trading signal generation rules, and the code examples are only simplified demonstrations.

 CTA strategies typically rely on large amounts of historical and real-time market data and use computer programs and algorithmic models to execute trading decisions. The advantage of this strategy is that it eliminates the impact of emotions and subjective judgments on trading, and improves the systematicness and discipline of trading.

It should be noted that the specific code implementation of the CTA strategy may vary depending on the needs of investors and the requirements of the trading platform. The above are just some common strategies and corresponding code examples. In actual applications, they need to be adjusted and optimized according to specific situations.

Quantitative CTA fund is a CTA fund that uses quantitative trading strategies. Its characteristics include:

1. Systematic trading: Quantitative CTA funds use computer programs and algorithms to make trading decisions and execute transactions based on strict rules and models, avoiding the influence of emotions and subjective judgments.

2. Diversification strategies: Quantitative CTA funds usually adopt a variety of different trading strategies, such as mean reversion, momentum, trend following, etc., to achieve a diversified investment portfolio, reduce risks and increase returns.

3. High-frequency trading: Some quantitative CTA funds adopt high-frequency trading strategies, that is, trading at time intervals of milliseconds or less, and making profits by taking advantage of tiny price fluctuations in the market.

4. Data-driven: Quantitative CTA funds rely on a large amount of historical and real-time market data to identify trading opportunities and formulate trading strategies through data analysis and model building.

5. Strict risk control: Quantitative CTA funds usually set strict risk control rules and stop-loss mechanisms to control risks and protect investors’ interests.

6. High degree of automation: The trading process of quantitative CTA funds is highly automated, and transactions are automatically executed through the transaction execution system and algorithm model, reducing human intervention and trading errors.

7. High degree of transparency: Quantitative CTA funds usually provide detailed transaction reports and performance data so that investors can understand the operation and performance of the fund.