1、股价月级别趋势下行，周级别开始反弹
2、止损5%，止盈30%
3、仓位平均分配，持仓5只
低位反弹，价值投资系列
敦行致远u2025-10-29 发布330
5
1
收益&风险
源码
评论
2025-10-30 01:20:30
To create a stock trading strategy based on the description you provided, you can follow these steps:
Step 1: Setting up the Environment
Navigate to "My Strategies" in SuperMind's platform and choose "Strategy Research". This is where you will start creating your new strategy.
Step 2: Defining Your Strategy Logic
Code Sample: In Python, you'd write your logic on the left side of the interface:
# Define a function to initialize your strategy
def init(context):
# Set stock symbol
global g_stock_symbol
g_stock_symbol = 'AAPL' # Just for example. You will replace this with desired symbol
**Main Strategy Implementation:**
```python
def handle_bar(context, bar_data):
# Check the monthly trend and weekly performance
if monthly_trend_down(bar_data) and weekly_trend_upward(bar_data):
# If both conditions are met
# Place orders to enter positions with a 5% stop-loss and 30% take-profit
order_all_stocks(context, max_loss=stop_loss_percentage, profit_target=profit_target)
else:
if not weekly_trend_upward(bar_data):
# If only monthly trend is down but weekly isn't showing positive signs
# Place orders to sell all holdings
order_all_stocks(context, action='sell', amount=5)
# Set allocation for each stock
allocate_stocks(context)
Step 3: Running the Backtest
Navigate to the right side of your strategy's interface and select a time period to backtest your strategy using historical data.
Key Functions:
monthly_trend_down(bar_data): A function that checks if there is an overall downward trend over the past month.weekly_trend_upward(bar_data): Determines whether weekly prices are trending upwards.order_all_stocks(context, max_loss, profit_target): Sends orders to enter positions with specified stop-loss and take-profit levels for all stocks in your portfolio.allocate_stocks(context): Assigns equal weights or any other strategy for stock allocation based on your preferences.
Backtest Parameters:
Choose the stock symbols you're interested in. In this case, 'AAPL' is just a placeholder symbol that needs to be replaced with desired stock symbols.
Adjust parameters like stop-loss and profit-target depending on how aggressive or conservative you want to make your strategy.
Finalizing Your Strategy:
Once everything looks good, review your strategy one last time before running the backtest. Make sure all conditions are clearly defined and correctly implemented in Python.
After you've tested your strategy with historical data using SuperMind's platform, analyze results to see how it performed under different market conditions and make adjustments as necessary.
Remember this is a general guideline, specific details might vary depending on what functions and tools the SuperMind trading platform provides.
2025-10-30 02:20:35
To create a stock trading strategy based on the description you provided, you can follow these steps:
Step 1: Setting up the Environment
Navigate to "My Strategies" in SuperMind's platform and choose "Strategy Research". This is where you will start creating your new strategy.
Step 2: Defining Your Strategy Logic
Code Sample: In Python, you'd write your logic on the left side of the interface:
# Define a function to initialize your strategy
def init(context):
# Set stock symbol
global g_stock_symbol
g_stock_symbol = 'AAPL' # Just for example. You will replace this with desired symbol
**Main Strategy Implementation:**
```python
def handle_bar(context, bar_data):
# Check the monthly trend and weekly performance
if monthly_trend_down(bar_data) and weekly_trend_upward(bar_data):
# If both conditions are met
# Place orders to enter positions with a 5% stop-loss and 30% take-profit
order_all_stocks(context, max_loss=stop_loss_percentage, profit_target=profit_target)
else:
if not weekly_trend_upward(bar_data):
# If only monthly trend is down but weekly isn't showing positive signs
# Place orders to sell all holdings
order_all_stocks(context, action='sell', amount=5)
# Set allocation for each stock
allocate_stocks(context)
Step 3: Running the Backtest
Navigate to the right side of your strategy's interface and select a time period to backtest your strategy using historical data.
Key Functions:
monthly_trend_down(bar_data): A function that checks if there is an overall downward trend over the past month.weekly_trend_upward(bar_data): Determines whether weekly prices are trending upwards.order_all_stocks(context, max_loss, profit_target): Sends orders to enter positions with specified stop-loss and take-profit levels for all stocks in your portfolio.allocate_stocks(context): Assigns equal weights or any other strategy for stock allocation based on your preferences.
Backtest Parameters:
Choose the stock symbols you're interested in. In this case, 'AAPL' is just a placeholder symbol that needs to be replaced with desired stock symbols.
Adjust parameters like stop-loss and profit-target depending on how aggressive or conservative you want to make your strategy.
Finalizing Your Strategy:
Once everything looks good, review your strategy one last time before running the backtest. Make sure all conditions are clearly defined and correctly implemented in Python.
After you've tested your strategy with historical data using SuperMind's platform, analyze results to see how it performed under different market conditions and make adjustments as necessary.
Remember this is a general guideline, specific details might vary depending on what functions and tools the SuperMind trading platform provides.
2025-10-30 06:20:22
To create a stock trading strategy based on the description you provided, you can follow these steps:
Step 1: Setting up the Environment
Navigate to "My Strategies" in SuperMind's platform and choose "Strategy Research". This is where you will start creating your new strategy.
Step 2: Defining Your Strategy Logic
Code Sample: In Python, you'd write your logic on the left side of the interface:
# Define a function to initialize your strategy
def init(context):
# Set stock symbol
global g_stock_symbol
g_stock_symbol = 'AAPL' # Just for example. You will replace this with desired symbol
**Main Strategy Implementation:**
```python
def handle_bar(context, bar_data):
# Check the monthly trend and weekly performance
if monthly_trend_down(bar_data) and weekly_trend_upward(bar_data):
# If both conditions are met
# Place orders to enter positions with a 5% stop-loss and 30% take-profit
order_all_stocks(context, max_loss=stop_loss_percentage, profit_target=profit_target)
else:
if not weekly_trend_upward(bar_data):
# If only monthly trend is down but weekly isn't showing positive signs
# Place orders to sell all holdings
order_all_stocks(context, action='sell', amount=5)
# Set allocation for each stock
allocate_stocks(context)
Step 3: Running the Backtest
Navigate to the right side of your strategy's interface and select a time period to backtest your strategy using historical data.
Key Functions:
monthly_trend_down(bar_data): A function that checks if there is an overall downward trend over the past month.weekly_trend_upward(bar_data): Determines whether weekly prices are trending upwards.order_all_stocks(context, max_loss, profit_target): Sends orders to enter positions with specified stop-loss and take-profit levels for all stocks in your portfolio.allocate_stocks(context): Assigns equal weights or any other strategy for stock allocation based on your preferences.
Backtest Parameters:
Choose the stock symbols you're interested in. In this case, 'AAPL' is just a placeholder symbol that needs to be replaced with desired stock symbols.
Adjust parameters like stop-loss and profit-target depending on how aggressive or conservative you want to make your strategy.
Finalizing Your Strategy:
Once everything looks good, review your strategy one last time before running the backtest. Make sure all conditions are clearly defined and correctly implemented in Python.
After you've tested your strategy with historical data using SuperMind's platform, analyze results to see how it performed under different market conditions and make adjustments as necessary.
Remember this is a general guideline, specific details might vary depending on what functions and tools the SuperMind trading platform provides.
2025-10-30 07:20:27
To create a stock trading strategy based on the description you provided, you can follow these steps:
Step 1: Setting up the Environment
Navigate to "My Strategies" in SuperMind's platform and choose "Strategy Research". This is where you will start creating your new strategy.
Step 2: Defining Your Strategy Logic
Code Sample: In Python, you'd write your logic on the left side of the interface:
# Define a function to initialize your strategy
def init(context):
# Set stock symbol
global g_stock_symbol
g_stock_symbol = 'AAPL' # Just for example. You will replace this with desired symbol
**Main Strategy Implementation:**
```python
def handle_bar(context, bar_data):
# Check the monthly trend and weekly performance
if monthly_trend_down(bar_data) and weekly_trend_upward(bar_data):
# If both conditions are met
# Place orders to enter positions with a 5% stop-loss and 30% take-profit
order_all_stocks(context, max_loss=stop_loss_percentage, profit_target=profit_target)
else:
if not weekly_trend_upward(bar_data):
# If only monthly trend is down but weekly isn't showing positive signs
# Place orders to sell all holdings
order_all_stocks(context, action='sell', amount=5)
# Set allocation for each stock
allocate_stocks(context)
Step 3: Running the Backtest
Navigate to the right side of your strategy's interface and select a time period to backtest your strategy using historical data.
Key Functions:
monthly_trend_down(bar_data): A function that checks if there is an overall downward trend over the past month.weekly_trend_upward(bar_data): Determines whether weekly prices are trending upwards.order_all_stocks(context, max_loss, profit_target): Sends orders to enter positions with specified stop-loss and take-profit levels for all stocks in your portfolio.allocate_stocks(context): Assigns equal weights or any other strategy for stock allocation based on your preferences.
Backtest Parameters:
Choose the stock symbols you're interested in. In this case, 'AAPL' is just a placeholder symbol that needs to be replaced with desired stock symbols.
Adjust parameters like stop-loss and profit-target depending on how aggressive or conservative you want to make your strategy.
Finalizing Your Strategy:
Once everything looks good, review your strategy one last time before running the backtest. Make sure all conditions are clearly defined and correctly implemented in Python.
After you've tested your strategy with historical data using SuperMind's platform, analyze results to see how it performed under different market conditions and make adjustments as necessary.
Remember this is a general guideline, specific details might vary depending on what functions and tools the SuperMind trading platform provides.
2025-10-30 09:20:26
To create a stock trading strategy based on the description you provided, you can follow these steps:
Step 1: Setting up the Environment
Navigate to "My Strategies" in SuperMind's platform and choose "Strategy Research". This is where you will start creating your new strategy.
Step 2: Defining Your Strategy Logic
Code Sample: In Python, you'd write your logic on the left side of the interface:
# Define a function to initialize your strategy
def init(context):
# Set stock symbol
global g_stock_symbol
g_stock_symbol = 'AAPL' # Just for example. You will replace this with desired symbol
**Main Strategy Implementation:**
```python
def handle_bar(context, bar_data):
# Check the monthly trend and weekly performance
if monthly_trend_down(bar_data) and weekly_trend_upward(bar_data):
# If both conditions are met
# Place orders to enter positions with a 5% stop-loss and 30% take-profit
order_all_stocks(context, max_loss=stop_loss_percentage, profit_target=profit_target)
else:
if not weekly_trend_upward(bar_data):
# If only monthly trend is down but weekly isn't showing positive signs
# Place orders to sell all holdings
order_all_stocks(context, action='sell', amount=5)
# Set allocation for each stock
allocate_stocks(context)
Step 3: Running the Backtest
Navigate to the right side of your strategy's interface and select a time period to backtest your strategy using historical data.
Key Functions:
monthly_trend_down(bar_data): A function that checks if there is an overall downward trend over the past month.weekly_trend_upward(bar_data): Determines whether weekly prices are trending upwards.order_all_stocks(context, max_loss, profit_target): Sends orders to enter positions with specified stop-loss and take-profit levels for all stocks in your portfolio.allocate_stocks(context): Assigns equal weights or any other strategy for stock allocation based on your preferences.
Backtest Parameters:
Choose the stock symbols you're interested in. In this case, 'AAPL' is just a placeholder symbol that needs to be replaced with desired stock symbols.
Adjust parameters like stop-loss and profit-target depending on how aggressive or conservative you want to make your strategy.
Finalizing Your Strategy:
Once everything looks good, review your strategy one last time before running the backtest. Make sure all conditions are clearly defined and correctly implemented in Python.
After you've tested your strategy with historical data using SuperMind's platform, analyze results to see how it performed under different market conditions and make adjustments as necessary.
Remember this is a general guideline, specific details might vary depending on what functions and tools the SuperMind trading platform provides.