1-200 Moving Average System: 두 판 사이의 차이
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(Created page with "'''1-200 Moving Average System''' is a trend-following trading strategy that uses the interaction between the 1-day and 200-day moving averages to generate buy and sell signals. This system is widely used in technical analysis to capture long-term trends and avoid false signals. ==Concept== The strategy relies on: *'''1-day moving average (1MA)''' – Represents the most recent closing price. *'''200-day moving average (200MA)''' – Represents the long-term trend. Trade...") |
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'''1-200 Moving Average System''' is a trend-following trading strategy that uses the | '''1-200 Moving Average System''' is a trend-following trading strategy that uses the relationship between the 1-day and 200-day moving averages to generate buy and sell signals. This system is widely used in technical analysis to capture long-term trends and avoid false signals. | ||
==Concept== | ==Concept== | ||
The strategy relies on: | The strategy relies on: | ||
6번째 줄: | 6번째 줄: | ||
Traders use this system to identify bullish and bearish market conditions based on the position of the 1-day moving average relative to the 200-day moving average. | Traders use this system to identify bullish and bearish market conditions based on the position of the 1-day moving average relative to the 200-day moving average. | ||
==Trading Rules== | ==Trading Rules== | ||
# | #'''Buy Signal''' – When the price (1MA) crosses above the 200MA, indicating an uptrend (Golden Cross). | ||
# | #'''Sell Signal''' – When the price (1MA) crosses below the 200MA, indicating a downtrend (Death Cross). | ||
==Example== | ==Example== | ||
A simple implementation of the 1-200 moving average system in Python:<syntaxhighlight lang="python"> | A simple implementation of the 1-200 moving average system in Python:<syntaxhighlight lang="python"> | ||
30번째 줄: | 30번째 줄: | ||
</syntaxhighlight> | </syntaxhighlight> | ||
==Advantages== | ==Advantages== | ||
* | *'''Simple to Implement''' | ||
* | ** Easy to calculate and interpret. | ||
* | *'''Effective for Trend Following''' | ||
**Helps capture long-term trends. | |||
*'''Reduces Market Noise''' | |||
**Filters out short-term fluctuations. | |||
==Limitations== | ==Limitations== | ||
* | * '''Lagging Indicator''' | ||
* | **Signals appear after trends have started. | ||
* | *'''Whipsaw Risk''' | ||
** Frequent false signals in sideways markets. | |||
*'''Not Suitable for Short-Term Trading''' | |||
**Designed for long-term trend identification. | |||
==Applications== | ==Applications== | ||
* | *'''Stock Trading'''**Commonly used in equity markets. | ||
* | *'''Forex Trading''' | ||
* | **Helps traders identify major currency trends. | ||
*'''Cryptocurrency''' | |||
**Used to navigate volatile crypto markets. | |||
==See Also== | ==See Also== | ||
*[[Moving Average]] | *[[Moving Average]] | ||
*[[Golden Cross]] | * [[Golden Cross]] | ||
*[[Death Cross]] | *[[Death Cross]] | ||
*[[Trend Following Strategy]] | *[[Trend Following Strategy]] | ||
*[[Technical Analysis]] | *[[Technical Analysis]] |
2025년 2월 26일 (수) 06:01 기준 최신판
1-200 Moving Average System is a trend-following trading strategy that uses the relationship between the 1-day and 200-day moving averages to generate buy and sell signals. This system is widely used in technical analysis to capture long-term trends and avoid false signals.
1 Concept[편집 | 원본 편집]
The strategy relies on:
- 1-day moving average (1MA) – Represents the most recent closing price.
- 200-day moving average (200MA) – Represents the long-term trend.
Traders use this system to identify bullish and bearish market conditions based on the position of the 1-day moving average relative to the 200-day moving average.
2 Trading Rules[편집 | 원본 편집]
- Buy Signal – When the price (1MA) crosses above the 200MA, indicating an uptrend (Golden Cross).
- Sell Signal – When the price (1MA) crosses below the 200MA, indicating a downtrend (Death Cross).
3 Example[편집 | 원본 편집]
A simple implementation of the 1-200 moving average system in Python:
import pandas as pd
import matplotlib.pyplot as plt
# Load historical stock data
df = pd.read_csv("stock_prices.csv")
# Compute moving averages
df["1MA"] = df["Close"]
df["200MA"] = df["Close"].rolling(window=200).mean()
# Generate buy and sell signals
df["Signal"] = (df["1MA"] > df["200MA"]).astype(int)
# Plot the data
plt.plot(df["Close"], label="Stock Price")
plt.plot(df["200MA"], label="200-Day Moving Average", linestyle="dashed")
plt.legend()
plt.show()
4 Advantages[편집 | 원본 편집]
- Simple to Implement
- Easy to calculate and interpret.
- Effective for Trend Following
- Helps capture long-term trends.
- Reduces Market Noise
- Filters out short-term fluctuations.
5 Limitations[편집 | 원본 편집]
- Lagging Indicator
- Signals appear after trends have started.
- Whipsaw Risk
- Frequent false signals in sideways markets.
- Not Suitable for Short-Term Trading
- Designed for long-term trend identification.
6 Applications[편집 | 원본 편집]
- Stock Trading**Commonly used in equity markets.
- Forex Trading
- Helps traders identify major currency trends.
- Cryptocurrency
- Used to navigate volatile crypto markets.