VNM trade ideas
Long-term opportunity to buy value and growth stocks in VinamilkBased on the monthly chart, Vinamilk has been forming a bearish Elliott wave 5 for many years now. The current position is supported by the demand zone I've marked in blue (second from the bottom), but there is no support from the rising trendline yet. Therefore, the market is likely to continue its decline in the near future to level 5 before embarking on a new growth cycle. The 45-52k zone presents a good opportunity to accumulate long-term holdings before breaking out of the untested supply zone I've marked in blue at the top, around 136-150k. Once the peak is reached, I will analyze the new Elliott wave pattern for you. Stay tuned!
Vinamilk: VNM (Fair value & slow growth)● Vietnam Dairy Products Joint Stock Company (VNM) was established in 1976. The Company has its main business in processing, production, trade, import and export of dairy products and other nutritious ones, under the model of a joint stock company since 2003. VNM is the biggest dairy company with a market share of over 50% in Vietnam dairy industry. VNM's products are exported to 57 countries.
● Milk consumption per capita in Vietnam was only 28 liters in 2021, lower than Thailand (35 liters) and Singapore (45 liters). VS each person in Vietnam consumes around 42.5 liters of beer annually.
● I believe every VNM investor should hold a glass of vinamilk instead glass of beer or soft drink to have a better life.
Sun 10/03/2024
VNM phase DHo Chi Minh Stock Exchange (HOSE), formerly known as HCM Securities Trading Center, is a stock exchange in Ho Chi Minh City, Vietnam. It was established in 1998 under Decision No. 127/1998/QD-TTg of the Prime Minister of Vietnam. HCM Securities Trading Center officially opened on July 20, 2000, and had its first trading session on July 28, 2000, with two listed companies and six security company members.
Locimport pandas as pd
import ta.volatility as vol
# Load stock price data into a pandas DataFrame
df = pd.read_csv('stock_prices.csv')
# Calculate Bollinger Bands
df , df , df = vol.bollinger_hband(df ), vol.bollinger_mavg(df ), vol.bollinger_lband(df )
# Filter stock price movements that are above the upper Bollinger Band
df = df > df
# Filter stock price movements that are below the lower Bollinger Band
df = df < df
# Print the filtered stock price movements
print(df [df | df ])
