Methodology
To independently verify and assess the EA system’s trading plan, I will use a combination of technical indicators and pattern recognition. The key steps include:
- Calculate Moving Averages (MA): Specifically, the 5-period Simple Moving Average (SMA) and the 288-period Bollinger Bands.
- Analyze Bollinger Bands: Identify the upper and lower bands and check for any recent breakouts or crossovers.
- Pattern Recognition: Look for specific candlestick patterns and price action that might indicate a trend or reversal.
- Volume Analysis: Consider the volume to confirm the strength of the trend.
- Session-Specific Considerations: Adjust the analysis based on the current trading session (e.g., Asian, London, NY).
Data Preparation
First, let’s extract and prepare the necessary data from the provided K-line data.
`python
import pandas as pd
Convert the data into a DataFrame
data = [
“2026.02.04 05:55,4942.26000,4942.26000,4942.26000,4942.26000,1”,
“2026.02.04 05:50,4939.12000,4944.76000,4933.20000,4942.22000,989”,
# … (all 432 bars)
“2026.02.02 17:00,4639.88000,4645.26000,4603.58000,4627.30000,2415”
]
Split the data and create a DataFrame
df = pd.DataFrame([line.split(‘,’) for line in data], columns=[‘Timestamp’, ‘Open’, ‘High’, ‘Low’, ‘Close’, ‘Volume’])
df[‘Timestamp’] = pd.to_datetime(df[‘Timestamp’], format=’%Y.%m.%d %H:%M’)
df[[‘Open’, ‘High’, ‘Low’, ‘Close’, ‘Volume’]] = df[[‘Open’, ‘High’, ‘Low’, ‘Close’, ‘Volume’]].astype(float)
Calculate the 5-period SMA
df[‘SMA_5’] = df[‘Close’].rolling(window=5).mean()
Calculate the 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’]
Display the last few rows to check the calculations
print(df.tail())
`
Technical Indicator Analysis
- Moving Averages (SMA_5):
– Check if the 5-period SMA is trending upward.
– Verify if the 5-period SMA has crossed above the lower Bollinger Band.
- Bollinger Bands:
– Check the position of the current price relative to the Bollinger Bands.
– Identify any recent breakouts or crossovers.
Pattern Recognition
- Candlestick Patterns: Look for bullish or bearish patterns such as engulfing, hammer, or doji.
- Price Action: Analyze the recent price action for any signs of a trend or reversal.
Volume Analysis
- Volume Confirmation: Higher volume during a breakout or trend continuation can confirm the strength of the move.
Session-Specific Considerations
- Current Session: The latest timestamp is 05:55 UTC+8, which is within the Asian session. This session typically has lower volatility and ranging behavior.
Analysis
Let’s analyze the latest data points:
`python
Latest close price
latest_close = df[‘Close’].iloc[-1]
Latest SMA_5
latest_sma_5 = df[‘SMA_5’].iloc[-1]
Latest Bollinger Bands
latest_bb_lower = df[‘BB_Lower’].iloc[-1]
Check if SMA_5 has crossed above the lower Bollinger Band
sma_crossed_bb = (df[‘SMA_5’].iloc[-1] > df[‘BB_Lower’].iloc[-1]) and (df[‘SMA_5’].iloc[-2] <= df['BB_Lower'].iloc[-2])
Check if SMA_5 is trending upward
sma_trending_up = df[‘SMA_5’].iloc[-1] > df[‘SMA_5’].iloc[-2]
Print the results
print(f”Latest Close: {latest_close}”)
print(f”Latest SMA_5: {latest_sma_5}”)
print(f”Latest BB Lower: {latest_bb_lower}”)
print(f”SMA_5 Crossed Above BB Lower: {sma_crossed_bb}”)
print(f”SMA_5 Trending Up: {sma_trending_up}”)
`
Final Review and Evaluation
- SMA_5 Trending Up: If the 5-period SMA is trending upward and has crossed above the lower Bollinger Band, it supports a long buy signal.
- Session Consideration: Since we are in the Asian session, we need to be cautious about low liquidity and potential false signals.
Final Trading Signal
Based on the analysis, if the conditions are met, we will output a long buy signal. Otherwise, we will output a Maintain Watch signal.
`python
if sma_trending_up and sma_crossed_bb:
direction_signal = “Long”
trade_entry_price = latest_close
signal_strength = 5 # Moderate strength
stop_loss_price = latest_bb_lower
take_profit_price = latest_close + (latest_close – latest_bb_lower) * 1.5 # Example take profit level
else:
direction_signal = “Watch”
signal_strength = 0
support_level = df[‘Low’].min()
resistance_level = df[‘High’].max()
Output the final trading signal
if direction_signal == “Long”:
print(f”Direction signal: {direction_signal}”)
print(f”Trade entry price: >>> {trade_entry_price} <<<")
print(f”Signal Strength: =>> {signal_strength} <<= ")
print(f"Stop-Loss price: <span class="support"> {stop_loss_price} </span>")
print(f"Take-Profit price: <span class="resistance"> {take_profit_price} </span>")
else:
print(f”Direction signal: {direction_signal}”)
print(f”Latest Close: >>> {latest_close} <<<")
print(f”Signal Strength: =>> {signal_strength} <<= ")
print(f"Support level: <span class="support"> {support_level} </span>")
print(f"Resistance level: <span class="resistance"> {resistance_level} </span>")
`
Conclusion
The final trading signal will be determined by the above conditions. If the 5-period SMA is trending upward and has crossed above the lower Bollinger Band, a long buy signal will be generated. Otherwise, a Maintain Watch signal will be output.