Gold price analysis forecast 2023

How to get gold data from Yahoo Finance in Python?

Analyzing and forecasting the price of gold is a complex task that involves considering a variety of factors and methodologies. Here's a general overview of how gold price analysis and forecasting can be conducted:

Technical Analysis: This approach involves studying historical price charts and patterns to make predictions about future price movements. Analysts use tools like moving averages, support and resistance levels, and various technical indicators to identify trends and potential turning points in the gold market.

Fundamental Analysis: Fundamental analysis looks at the underlying economic, geopolitical, and macroeconomic factors that can impact the price of gold. Some key factors include:

a. Interest Rates: Gold is often seen as a hedge against inflation. When interest rates are low, the opportunity cost of holding gold (which doesn't pay interest) is also low, making gold more attractive to investors.

b. Currency Strength: Gold is priced in US dollars, so changes in the strength of the US dollar can influence gold prices. A weaker dollar tends to drive up gold prices, as it takes more dollars to buy the same amount of gold.

c. Geopolitical Events: Political instability, conflicts, and other geopolitical factors can create uncertainty in the financial markets and drive investors toward safe-haven assets like gold.

d. Supply and Demand: The supply and demand dynamics of gold also play a role. Factors such as mine production, central bank purchases or sales, and jewelry demand can impact prices.

e. Market Sentiment: Investor sentiment and speculative trading can lead to short-term price fluctuations. This is often seen in the futures and options markets.

Macro-Economic Analysis: Economists and analysts consider broader economic indicators such as GDP growth, unemployment rates, and consumer sentiment to gauge the overall health of the economy, which can indirectly influence gold prices.

Quantitative Models: Some analysts use mathematical models and algorithms to predict gold prices. These models may incorporate a combination of historical price data, economic indicators, and other variables to make forecasts.

Expert Opinions: Expert opinions and forecasts from reputable financial institutions, economists, and gold market analysts are also considered in forming price forecasts.

External Factors: Black Swan events, like the COVID-19 pandemic, can have a significant impact on gold prices. These events are hard to predict but are essential to consider in any forecasting analysis.

Machine Learning and AI: In recent years, machine learning and artificial intelligence techniques have been applied to gold price forecasting. These models can analyze vast amounts of data and identify patterns that may not be apparent through traditional analysis.

It's important to note that no forecasting method is foolproof, and gold prices can be influenced by unforeseen events and market sentiment. Analysts often provide a range of possible price scenarios rather than precise predictions.

Investors and analysts should also be aware of the limitations and risks associated with gold investing, including the potential for volatility and the fact that past performance is not necessarily indicative of future results. Diversifying one's investment portfolio and seeking advice from financial professionals are important steps in managing these risks.
Get Data from Yfinance:

 How to get a Gold price analysis chart?

Describe data table
<matplotlib.axes._subplots.AxesSubplot at 0x7fc7a3d08f90>

How to get a gold multiplicative decomposition chart?

Multiplicative decomposition chats
Run prophet with daily_seasonality=True to override this

This is the price of gold forecast till 2022

Now components describe gold prices' weekly monthly and yearly trend
Compare 'Gold Price - actual vs. predicted charts
Gold Price - actual vs. predicted charts
[Text(0, 0.5, 'Gold Price ($)'),
 Text(0.5, 0, 'Date'),
 Text(0.5, 1.0, 'Gold Price - actual vs. predicted')]
gold actual and predicted price


Test Statistic -1.604289 p-value 0.481439 # of Lags Used 9.000000 # of Observations Used 2887.000000
Critical Value (1%) -3.432617 Critical Value (5%) -2.862542 Critical Value (10%) -2.567303 dtype: float64


p-value is smaller than the indicated p-value

Test Statistic            1.582691
p-value                   0.010000
# of Lags                28.000000
Critical Value (10%)      0.347000
Critical Value (5%)       0.463000
Critical Value (2.5%)     0.574000
Critical Value (1%)       0.739000
dtype: float64

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