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 lower band.
- Trend Analysis: Analyze the direction and strength of the trend using the MA(5) and BB(288).
- Pattern Recognition: Look for specific candlestick patterns and price action that support or contradict the trend.
- Session Considerations: Consider the specific market session (e.g., Asian, London, NY) and its typical characteristics.
Data Preparation
First, let’s parse the provided data and calculate the necessary indicators.
#### Data Parsing
`python
import pandas as pd
Parse the data
data = [
# … (all the data points provided)
]
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)
`
#### Indicator Calculations
`python
Calculate 5-period Moving Average (MA(5))
df[‘MA_5’] = df[‘Close’].rolling(window=5).mean()
Calculate 288-period Bollinger Bands (BB(288))
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)
`
Trend Analysis
We need to check if the MA(5) has broken upward through the lower band of the Bollinger Bands (BB(288)) and if the moving average is trending upward.
`python
Check if MA(5) has broken upward through the lower band of BB(288)
df[‘MA_Breaks_BB_Lower’] = (df[‘MA_5’] > df[‘BB_Lower’]) & (df[‘MA_5’].shift(1) <= df['BB_Lower'])
Check if MA(5) is trending upward
df[‘MA_Trending_Up’] = df[‘MA_5’] > df[‘MA_5’].shift(1)
`
Pattern Recognition
We will look for bullish candlestick patterns and other price action signals that support a long position.
`python
Example: Bullish Engulfing Pattern
df[‘Bullish_Engulfing’] = (df[‘Close’] > df[‘Open’]) & (df[‘Close’].shift(1) df[‘Open’].shift(1)) & (df[‘Open’] < df['Close'].shift(1))
Example: Hammer Pattern
df[‘Hammer’] = (df[‘Close’] > df[‘Open’]) & (df[‘Close’] – df[‘Low’] > 2 (df[‘High’] – df[‘Close’])) & ((df[‘High’] – df[‘Low’]) > 3 (df[‘Open’] – df[‘Close’]))
`
Session Considerations
We need to consider the current market session and its typical characteristics.
`python
Define the current time
current_time = df.index[-1].time()
Determine the market session
if current_time >= pd.Timestamp(’09:00′).time() and current_time < pd.Timestamp('14:00').time():
session = ‘Asian’
elif current_time >= pd.Timestamp(’15:00′).time() and current_time < pd.Timestamp('16:00').time():
session = ‘London Open’
elif current_time >= pd.Timestamp(’20:00′).time() and current_time < pd.Timestamp('22:00').time():
session = ‘London-NY Overlap’
elif current_time >= pd.Timestamp(’20:00′).time() and current_time < pd.Timestamp('01:00').time():
session = ‘NY Session’
elif current_time >= pd.Timestamp(’02:00′).time() and current_time < pd.Timestamp('06:00').time():
session = ‘Low Liquidity’
else:
session = ‘Other’
`
Final Analysis
Now, let’s combine all the information to make a final decision.
`python
Get the latest close price
latest_close = df[‘Close’].iloc[-1]
Get the latest MA(5) and BB(Lower) values
latest_MA_5 = df[‘MA_5’].iloc[-1]
latest_BB_Lower = df[‘BB_Lower’].iloc[-1]
Check if the conditions are met
if df[‘MA_Breaks_BB_Lower’].iloc[-1] and df[‘MA_Trending_Up’].iloc[-1]:
# Check for bullish patterns
if df[‘Bullish_Engulfing’].iloc[-1] or df[‘Hammer’].iloc[-1]:
# Determine the signal strength
signal_strength = 7 # Example value, can be adjusted based on additional factors
trade_entry_price = latest_close
stop_loss_price = latest_BB_Lower
take_profit_price = latest_close + 2 * (latest_close – latest_BB_Lower)
# Output the trading signal
print(f”Direction signal: Long”)
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:
# If no bullish pattern, maintain watch
print(f”Direction signal: Watch”)
print(f”Latest Close: >>> {latest_close} <<<")
print(f”Signal Strength: =>> 0 <<= ")
print(f"Support level: <span class="support"> {latest_BB_Lower} </span>")
print(f"Resistance level: <span class="resistance"> {latest_close + 2 * (latest_close – latest_BB_Lower)} </span>")
else:
# If conditions are not met, maintain watch
print(f”Direction signal: Watch”)
print(f”Latest Close: >>> {latest_close} <<<")
print(f”Signal Strength: =>> 0 <<= ")
print(f"Support level: <span class="support"> {latest_BB_Lower} </span>")
print(f"Resistance level: <span class="resistance"> {latest_close + 2 * (latest_close – latest_BB_Lower)} </span>")
`
Conclusion
Based on the above analysis, we will output the final trading signal. The exact values for the trade entry price, stop-loss, and take-profit will depend on the actual calculations from the data. The signal strength and levels will be determined based on the current market conditions and the presence of bullish patterns.