XAUUSD价格趋势分析 (2026-02-04 01:45:01)

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: Look for specific candlestick patterns and price action that may indicate a potential reversal or continuation.
Volume Analysis: Consider the volume to confirm the strength of the move.
Session Analysis: Take into account the specific session characteristics (e.g., Asian, London, NY sessions).

Data Preparation

First, let’s prepare the data and calculate the necessary indicators.

#### 1. Calculate the 5-Period Moving Average (MA(5))

The 5-period moving average is calculated as the average of the closing prices over the last 5 periods.

#### 2. Calculate the 288-Period Bollinger Bands (BB(288))

The Bollinger Bands are calculated as follows:

Middle Band (MB): 288-period Simple Moving Average (SMA) of the closing prices.
Upper Band (UB): MB + 2 * Standard Deviation (SD) of the closing prices over the last 288 periods.
Lower Band (LB): MB – 2 * Standard Deviation (SD) of the closing prices over the last 288 periods.

Calculation and Analysis

Let’s start by calculating the MA(5) and BB(288) for the given data.

`python

import pandas as pd

import numpy as np

Convert the data into a DataFrame

data = [

# … (all the provided data)

]

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(288))

df[‘MB_288’] = df[‘Close’].rolling(window=288).mean()

df[‘SD_288’] = df[‘Close’].rolling(window=288).std()

df[‘UB_288’] = df[‘MB_288’] + 2 * df[‘SD_288’]

df[‘LB_288’] = df[‘MB_288’] – 2 * df[‘SD_288’]

Display the last few rows of the DataFrame to check the calculations

print(df.tail())

Trend Analysis

Now, let’s analyze the trend and the current market state.

#### 1. Check if MA(5) has broken downward through the upper band of the Bollinger Bands (UB(288))

We need to check if the MA(5) has crossed below the UB(288) and if the MA(5) is trending downward.

#### 2. Analyze the Current Market State

Candlestick Patterns: Look for bearish patterns like Bearish Engulfing, Shooting Star, etc.
Volume: Check if the volume is increasing on the downside.
Session Characteristics: Consider the time of day and the typical behavior during that session.

Final Analysis

Let’s perform the final analysis and determine the trading signal.

`python

Check if MA(5) has broken downward through the upper band of the Bollinger Bands (UB(288))

df[‘MA_5_Below_UB_288’] = (df[‘MA_5’] = df[‘UB_288’])

Check if MA(5) is trending downward

df[‘MA_5_Trending_Downward’] = df[‘MA_5’] < df['MA_5'].shift(1)

Filter the latest data point

latest_data = df.iloc[-1]

Determine the trading signal

if latest_data[‘MA_5_Below_UB_288’] and latest_data[‘MA_5_Trending_Downward’]:

# Check for additional confirmation (e.g., bearish candlestick pattern, high volume)

if latest_data[‘Close’] df[‘Volume’].rolling(window=5).mean().iloc[-1]:

# Plan Short

direction_signal = “Short”

trade_entry_price = latest_data[‘Close’]

signal_strength = -10 # Strong short signal

stop_loss_price = latest_data[‘High’] + 2 * (latest_data[‘High’] – latest_data[‘Low’])

take_profit_price = latest_data[‘Low’] – 2 * (latest_data[‘High’] – latest_data[‘Low’])

else:

# Maintain Watch

direction_signal = “Watch”

latest_close = latest_data[‘Close’]

signal_strength = 0

support_level = df[‘Low’].min()

resistance_level = df[‘High’].max()

else:

# Maintain Watch

direction_signal = “Watch”

latest_close = latest_data[‘Close’]

signal_strength = 0

support_level = df[‘Low’].min()

resistance_level = df[‘High’].max()

Output the final trading signal

if direction_signal == “Watch”:

print(f”Direction signal: {direction_signal}”)

print(f”Latest Close: >>> {latest_close} <<<")

print(f”Signal Strength: =>> {signal_strength} <<= ")

print(f"Support level: {support_level} ")

print(f"Resistance level: {resistance_level} ")

elif direction_signal == “Short”: