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 (BB(288)).
- Bollinger Bands (BB): Calculate the 288-period Bollinger Bands to identify the upper and lower bands.
- Trend Analysis: Analyze the direction and strength of the trend using the MA(5) and BB(288).
- Pattern Recognition: Identify any significant K-line patterns that may indicate a change in market sentiment.
- Volatility Considerations: Consider the specific time of day and its typical volatility characteristics.
Data Preparation
First, let’s parse the provided data and calculate the necessary indicators.
#### Step 1: Parse the Data
We will parse the data into a structured format and then calculate the required indicators.
`python
import pandas as pd
import numpy as np
Parse the data
data = [
# … (all the data points)
]
Convert to DataFrame
df = pd.DataFrame(data, columns=[‘Timestamp’, ‘Open’, ‘High’, ‘Low’, ‘Close’, ‘Volume’])
df[‘Timestamp’] = pd.to_datetime(df[‘Timestamp’], format=’%Y.%m.%d %H:%M’)
df.set_index(‘Timestamp’, inplace=True)
Convert prices to float
df[[‘Open’, ‘High’, ‘Low’, ‘Close’]] = df[[‘Open’, ‘High’, ‘Low’, ‘Close’]].astype(float)
`
#### Step 2: Calculate Indicators
Next, we calculate the 5-period Moving Average (MA(5)) and the 288-period Bollinger Bands (BB(288)).
`python
Calculate 5-period Moving Average
df[‘MA_5’] = df[‘Close’].rolling(window=5).mean()
Calculate 288-period Bollinger Bands
window = 288
df[‘BB_Middle’] = df[‘Close’].rolling(window=window).mean()
df[‘BB_STD’] = df[‘Close’].rolling(window=window).std()
df[‘BB_Upper’] = df[‘BB_Middle’] + 2 * df[‘BB_STD’]
df[‘BB_Lower’] = df[‘BB_Middle’] – 2 * df[‘BB_STD’]
Drop rows with NaN values
df.dropna(inplace=True)
`
Analysis
Now, let’s analyze the current market state and verify the EA system’s trading plan.
#### Step 1: Trend Analysis
- MA(5) and BB(288) Relationship: Check if the MA(5) has broken downward through the upper band of the BB(288).
`python
Check if MA(5) has broken downward through the upper band of BB(288)
df[‘MA_5_Below_BB_Upper’] = (df[‘MA_5’] = df[‘BB_Upper’].shift(1))
`
#### Step 2: Current Market State
- Latest Close Price: The latest close price is
4939.45. - Support and Resistance Levels: Use today’s high/low and pivot points for intraday support and resistance levels.
`python
latest_close = df[‘Close’].iloc[-1]
support_level = df[‘Low’].min() # Using the lowest low as support
resistance_level = df[‘High’].max() # Using the highest high as resistance
`
#### Step 3: Volatility Considerations
- Time of Day: The current time is
02:10(UTC+8 Beijing Time), which falls within the low liquidity period (02:00-06:00). False breakouts are common during this time, so we need to be cautious.
Verification of EA System’s Trading Plan
- EA Signal: The EA system suggests a short sell based on the MA(5) breaking downward through the upper band of the BB(288) and the moving average trending downward.
- Independent Analysis:
– MA(5) and BB(288) Relationship: Check if the MA(5) has indeed broken downward through the upper band of the BB(288).
– Trend Direction: Verify if the MA(5) is trending downward.
– Market Conditions: Consider the low liquidity period and potential false breakouts.
`python
Check the last few bars for the MA(5) and BB(288) relationship
last_few_bars = df.tail(5)
ma_5_below_bb_upper = last_few_bars[‘MA_5_Below_BB_Upper’].any()
Check if MA(5) is trending downward
ma_5_trending_downward = (last_few_bars[‘MA_5’].diff().dropna() < 0).all()
Output the results
if ma_5_below_bb_upper and ma_5_trending_downward:
print(“The MA(5) has broken downward through the upper band of the BB(288) and is trending downward.”)
else:
print(“The MA(5) has not broken downward through the upper band of the BB(288) or is not trending downward.”)
`
Final Trading Signal
Based on the independent analysis, if the conditions are met, we can output the trading signal. If not, we will maintain a watch signal.
`python
if ma_5_below_bb_upper and ma_5_trending_downward:
# Calculate the trade entry price, stop-loss, and take-profit
trade_entry_price = latest_close
stop_loss_price = resistance_level
take_profit_price = support_level
signal_strength = -5 # Example signal strength for short
# Output the trading signal
print(f”Direction signal: Short”)
print(f”Trade entry price: >>> {trade_entry_price} <<<")
print(f”Signal Strength: =>> {signal_strength} <<= ")
print(f"Stop-Loss price: <span class="resistance"> {stop_loss_price} </span> ")
print(f"Take-Profit price: <span class="support"> {take_profit_price} </span> ")
else:
# Output the watch signal
print(f”Direction signal: Watch”)
print(f”Latest Close: >>> {latest_close} <<<")
print(f”Signal Strength: =>> 0 <<= ")
print(f"Support level: <span class="support"> {support_level} </span> ")
print(f"Resistance level: <span class="resistance"> {resistance_level} </span> ")
`
Conclusion
- If the conditions are met: The MA(5) has broken downward through the upper band of the BB(288) and is trending downward, we will output a short sell signal.
- If the conditions are not met: We will output a watch signal.
This approach ensures that the trading signal is based on objective and independent technical analysis, adhering to the specified requirements.