Methodology
To independently verify and provide a confidence assessment of the EA system’s trading plan, I will use the following technical analysis methods:
- Moving Averages (MA): Calculate the 5-period Moving Average (MA(5)) and the 288-period Bollinger Bands.
- Bollinger Bands (BB): Calculate the 288-period Bollinger Bands with a 2 standard deviation (SD) setting.
- Trend Analysis: Analyze the direction and strength of the trend using the MA(5) and BB.
- Support and Resistance Levels: Identify key support and resistance levels based on recent highs and lows.
- Volume Analysis: Consider the volume to confirm the strength of the price movements.
Data Preparation
First, let’s prepare the data and calculate the necessary indicators.
#### Step 1: Load and Prepare the Data
We will load the provided 5-minute K-line data and extract the required fields: Timestamp, Open, High, Low, Close, and Volume.
#### Step 2: Calculate the Indicators
- 5-period Moving Average (MA(5))
- 288-period Bollinger Bands (BB(288, 2 SD))
Calculation
Let’s start by calculating the MA(5) and BB(288, 2 SD).
`python
import pandas as pd
import numpy as np
Load the data
data = [
# … (all the provided data)
]
Create a DataFrame
df = pd.DataFrame(data, columns=[‘Timestamp’, ‘Open’, ‘High’, ‘Low’, ‘Close’, ‘Volume’])
Convert the Timestamp to datetime
df[‘Timestamp’] = pd.to_datetime(df[‘Timestamp’], format=’%Y.%m.%d %H:%M’)
Calculate the 5-period Moving Average (MA(5))
df[‘MA_5’] = df[‘Close’].rolling(window=5).mean()
Calculate the 288-period Bollinger Bands (BB(288, 2 SD))
df[‘BB_Middle’] = df[‘Close’].rolling(window=288).mean()
df[‘BB_Std’] = df[‘Close’].rolling(window=288).std()
df[‘BB_Upper’] = df[‘BB_Middle’] + 2 * df[‘BB_Std’]
df[‘BB_Lower’] = df[‘BB_Middle’] – 2 * df[‘BB_Std’]
Display the last few rows to check the calculations
print(df.tail())
`
Analysis
Now, let’s analyze the current market state and the trading signal.
#### Step 1: Check the Current Market State
- Current Price and MA(5) Trend: Determine if the MA(5) is trending downward.
- Bollinger Bands Breakdown: Check if the MA(5) has broken through the upper band of the Bollinger Bands (BB(288, 2 SD)).
#### Step 2: Verify the Trading Signal
- Short Sell Signal: Confirm if the conditions for a short sell are met.
Results
Let’s analyze the latest data points to determine the current market state and the validity of the trading signal.
`python
Get the latest data point
latest_data = df.iloc[-1]
previous_data = df.iloc[-2]
Check the current market state
ma_5_trending_down = latest_data[‘MA_5’] < previous_data['MA_5']
ma_5_below_bb_upper = latest_data[‘MA_5’] < latest_data['BB_Upper']
Output the results
if ma_5_trending_down and ma_5_below_bb_upper:
print(“The MA(5) is trending downward and has broken through the upper band of the Bollinger Bands (BB(288, 2 SD)).”)
else:
print(“The conditions for a short sell are not met.”)
`
Final Trading Signal
Based on the analysis, we will output the final trading signal.
#### Support and Resistance Levels
- Support Level: Use the recent low.
- Resistance Level: Use the recent high.
`python
Determine the support and resistance levels
support_level = df[‘Low’].iloc[-20:].min()
resistance_level = df[‘High’].iloc[-20:].max()
Latest close price
latest_close = latest_data[‘Close’]
Output the final trading signal
if ma_5_trending_down and ma_5_below_bb_upper:
signal_strength = -5 # Example signal strength, can be adjusted
stop_loss = resistance_level
take_profit = support_level
print(f"Direction signal: ShortnTrade entry price: >>> {latest_close} <<<nSignal Strength: <span class="signal-strength"> -{signal_strength} </span>=nStop-Loss price: <span class="resistance"> {stop_loss} </span> nTake-Profit price: <span class="support"> {take_profit} </span>")
else:
print(f"Direction signal: WatchnLatest Close: >>> {latest_close} <<<nSignal Strength: <span class="signal-strength"> 0 </span>=nSupport level: <span class="support"> {support_level} </span>nResistance level: <span class="resistance"> {resistance_level} </span>")
`
Conclusion
By analyzing the 5-minute K-line data and calculating the necessary indicators, we can independently verify the EA system’s trading plan. If the conditions for a short sell are met, we will output a short sell signal. Otherwise, we will maintain a watch signal.
Please run the above code in a Python environment to get the final trading signal.