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 (2σ) width.
- 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 price action.
- Market Session Considerations: Account for the specific market session characteristics (e.g., Asian, London, New York sessions).
Data Preparation
First, let’s parse the provided data and calculate the necessary indicators.
`python
import pandas as pd
import numpy as np
Parse the data
data = [
# … (all the provided data)
]
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)
Calculate the 5-period Moving Average (MA(5))
df[‘MA_5’] = df[‘Close’].rolling(window=5).mean()
Calculate the 288-period Bollinger Bands (BB)
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())
`
Trend Analysis
Next, we will analyze the trend and the current state of the market.
`python
Check if the MA(5) has broken through the lower band of the Bollinger Bands (BB)
df[‘MA_5_Breaks_BB_Lower’] = (df[‘MA_5’] > df[‘BB_Lower’]) & (df[‘MA_5’].shift(1) <= df['BB_Lower'].shift(1))
Check if the MA(5) is trending upward
df[‘MA_5_Trending_Up’] = df[‘MA_5’] > df[‘MA_5’].shift(1)
Filter the latest data point
latest_data = df.iloc[-1]
Check the conditions
ma_5_breaks_bb_lower = latest_data[‘MA_5_Breaks_BB_Lower’]
ma_5_trending_up = latest_data[‘MA_5_Trending_Up’]
Determine the market session
current_time = latest_data.name
if 9 <= current_time.hour < 14:
session = ‘Asian’
elif 15 <= current_time.hour < 16:
session = ‘London Open’
elif 20 <= current_time.hour < 22:
session = ‘London-NY Overlap’
elif 22 <= current_time.hour or current_time.hour < 1:
session = ‘NY Session’
else:
session = ‘Low Liquidity’
Print the results
print(f”Current Time: {current_time}”)
print(f”Session: {session}”)
print(f”MA(5) Breaks BB Lower: {ma_5_breaks_bb_lower}”)
print(f”MA(5) Trending Up: {ma_5_trending_up}”)
`
Support and Resistance Levels
Identify key support and resistance levels based on recent price action.
`python
Identify key support and resistance levels
support_level = df[‘Low’].iloc[-20:].min()
resistance_level = df[‘High’].iloc[-20:].max()
Print the support and resistance levels
print(f”Support Level: {support_level}”)
print(f”Resistance Level: {resistance_level}”)
`
Final Trading Signal
Based on the above analysis, we can now determine the final trading signal.
`python
Determine the final trading signal
if ma_5_breaks_bb_lower and ma_5_trending_up:
direction_signal = ‘Long’
trade_entry_price = latest_data[‘Close’]
signal_strength = 7 # Adjust based on the strength of the signal
stop_loss_price = support_level
take_profit_price = resistance_level
else:
direction_signal = ‘Watch’
latest_close = latest_data[‘Close’]
signal_strength = 0
support_level = df[‘Low’].iloc[-20:].min()
resistance_level = df[‘High’].iloc[-20:].max()
Output the final trading signal
if direction_signal == ‘Watch’:
print(f”Direction signal: Watch”)
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>")
elif direction_signal == ‘Long’:
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>")
`
Summary
- Methodology: Used MA(5), Bollinger Bands (288), and trend analysis.
- Trend Analysis: Checked if MA(5) breaks through the lower band of BB and if it is trending upward.
- Support and Resistance: Identified key levels based on recent price action.
- Final Signal: Based on the analysis, determined the final trading signal.
By running the above code, you will get the final trading signal and the necessary details. If the conditions are met, the signal will be “Long”; otherwise, it will be “Watch”.