IT 위키 - 사용자 기여 [ko]

OHLC

2025-04-20T14:22:13Z

Slack: Open-High-Low-Close 문서로 넘겨주기

#넘겨주기 [[Open-High-Low-Close]]

Sortino Ratio

2025-02-26T18:58:32Z

Slack: 새 문서: '''Sortino Ratio''' is a financial metric used to measure the risk-adjusted return of an investment, similar to the Sharpe Ratio. However, unlike the Sharpe Ratio, the Sortino Ratio only considers downside risk, making it a more precise tool for evaluating investments where investors are primarily concerned with losses. ==Definition== The Sortino Ratio is calculated as: *Sortino Ratio = (Rp - Rf) / σd where: *'''Rp''' – Return o...

'''Sortino Ratio''' is a financial metric used to measure the risk-adjusted return of an investment, similar to the [[Sharpe Ratio]]. However, unlike the Sharpe Ratio, the Sortino Ratio only considers downside risk, making it a more precise tool for evaluating investments where investors are primarily concerned with losses.
==Definition==
The Sortino Ratio is calculated as:
*Sortino Ratio = (Rp - Rf) / σd
where:
*'''Rp''' – Return of the portfolio.
*'''Rf''' – Risk-free rate (e.g., U.S. Treasury rate).
*'''σd''' – Downside deviation (standard deviation of negative returns).
==Interpretation==
*'''Sortino Ratio > 1.0''' – Indicates strong risk-adjusted performance with low downside risk.
*'''0 < Sortino Ratio ≤ 1.0''' – Suggests moderate performance but with noticeable downside risk.
*'''Sortino Ratio < 0''' – Indicates that the investment underperforms relative to the risk-free rate, meaning higher risk without sufficient return.
==Example Calculation==
Suppose:
*Portfolio return (Rp) = 12%
*Risk-free rate (Rf) = 3%
*Downside deviation (σd) = 5%
The Sortino Ratio is:
*(12% - 3%) / 5% = 1.8
==Advantages==
*'''Focuses on downside risk'''
**Only penalizes volatility that leads to losses.
*'''Better than Sharpe Ratio for asymmetrical returns'''
**More effective for assets with skewed return distributions.
*'''More relevant for conservative investors'''
**Provides a clearer risk-adjusted measure for funds aiming to minimize losses.
==Limitations==
*'''Requires downside deviation calculation'''
**More complex than standard deviation used in the Sharpe Ratio.
*'''Can be misleading for low-volatility assets'''
**A low-risk asset with low returns may still have a high Sortino Ratio.
*'''Sensitive to the threshold for downside risk'''
**Different investors may define "negative returns" differently.
==Applications==
*'''Portfolio Management'''
**Used by fund managers to evaluate performance relative to downside risk.
*'''Risk-Adjusted Investment Analysis'''
**Helps investors choose funds or stocks with better downside protection.
*'''Hedge Funds and Alternative Investments'''
**Often preferred over the Sharpe Ratio for non-traditional assets.
==Comparison with Sharpe Ratio==
{| class="wikitable"
!Metric!!Formula!!Risk Considered
|-
|Sharpe Ratio||(Rp - Rf) / σp||Total risk (both upside and downside)
|-
|Sortino Ratio||(Rp - Rf) / σd||Only downside risk
|}
==See Also==
*[[Sharpe Ratio]]
*[[Information Ratio]]
*[[Risk-Adjusted Return]]
*[[Volatility (Finance)]]
*[[Portfolio Management]]

Open-High-Low-Close

2025-02-25T22:23:51Z

Slack: Created page with "'''Open-High-Low-Close (OHLC)''' refers to the four key price points recorded for a financial instrument during a specific time period. These values are used in technical analysis to assess price movement and market trends. ==Components== An OHLC data point consists of: *'''Open (O)''' – The price at which the asset starts trading in a given time period. *'''High (H)''' – The highest price reached during the time period. *'''Low (L)''' – The lowest price reached du..."

'''Open-High-Low-Close (OHLC)''' refers to the four key price points recorded for a financial instrument during a specific time period. These values are used in technical analysis to assess price movement and market trends.
==Components==
An OHLC data point consists of:
*'''Open (O)''' – The price at which the asset starts trading in a given time period.
*'''High (H)''' – The highest price reached during the time period.
*'''Low (L)''' – The lowest price reached during the time period.
*'''Close (C)''' – The price at which the asset finishes trading at the end of the time period.
==Representation==
OHLC data is commonly visualized using:
*'''OHLC Bars''' – A vertical line representing the high and low prices, with horizontal ticks indicating the open (left) and close (right).
*'''Candlestick Charts''' – A graphical representation where the body shows the range between open and close, and wicks (shadows) show the high and low.
==Example Data==
{| class="wikitable"
!Date!!Open!!High!!Low!!Close
|-
|2024-06-01||100.5||105.0||98.7||103.2
|-
|2024-06-02||103.2||106.5||101.4||105.8
|-
|2024-06-03||105.8||107.2||103.9||104.5
|}
==Example Implementation==
A simple way to plot OHLC data using Python:<syntaxhighlight lang="python">
import pandas as pd
import matplotlib.pyplot as plt
import mplfinance as mpf

# Sample OHLC data
data = pd.DataFrame({
"Open": [100.5, 103.2, 105.8],
"High": [105.0, 106.5, 107.2],
"Low": [98.7, 101.4, 103.9],
"Close": [103.2, 105.8, 104.5]
}, index=pd.to_datetime(["2024-06-01", "2024-06-02", "2024-06-03"]))

# Plot OHLC chart
mpf.plot(data, type='ohlc', style='charles', title="OHLC Chart")
plt.show()
</syntaxhighlight>
==Applications==
*<nowiki>**Technical Analysis** – Used in candlestick patterns and trend analysis.</nowiki>
*<nowiki>**Algorithmic Trading** – Provides structured data for automated trading models.</nowiki>
*<nowiki>**Risk Management** – Helps assess volatility and price behavior.</nowiki>
==See Also==
*[[Candlestick Chart]]
*[[Price Action]]
*[[Volume Weighted Average Price (VWAP)]]
*[[Trend Following Strategy]]
*[[Breakout Trading]]

Range Breakout System

2025-02-25T21:04:45Z

Slack: Created page with "'''Range Breakout System''' is a trading strategy that identifies and trades price breakouts from a predefined range. Traders use this system to capture momentum when an asset moves beyond a support or resistance level. ==Concept== The strategy is based on the assumption that when the price breaks above or below a well-defined range, it is likely to continue moving in that direction. The range is typically defined by: *'''High and low prices over a period''' – Example:..."

'''Range Breakout System''' is a trading strategy that identifies and trades price breakouts from a predefined range. Traders use this system to capture momentum when an asset moves beyond a support or resistance level.
==Concept==
The strategy is based on the assumption that when the price breaks above or below a well-defined range, it is likely to continue moving in that direction. The range is typically defined by:
*'''High and low prices over a period''' – Example: The highest and lowest prices over the past 20 days.
*'''Support and resistance levels''' – Key levels where price repeatedly reverses.
*'''Opening range''' – The high and low of the first trading hour.
==Trading Rules==
#'''Identify the range:''' Define the breakout levels based on past price action.
#'''Buy Signal:''' When the price breaks above the range.
#'''Sell Signal:''' When the price breaks below the range.
#'''Stop-Loss:''' Placed slightly below (for long trades) or above (for short trades) the breakout level.
#'''Take Profit:''' Set using risk-reward ratios or trailing stops.
==Example==
A simple implementation of a range breakout system using Python:<syntaxhighlight lang="python">
import pandas as pd
import matplotlib.pyplot as plt

# Load historical stock data
df = pd.read_csv("stock_prices.csv")

# Define the range (e.g., last 20 days high and low)
df["High_20"] = df["High"].rolling(window=20).max()
df["Low_20"] = df["Low"].rolling(window=20).min()

# Generate buy and sell signals
df["Buy_Signal"] = df["Close"] > df["High_20"]
df["Sell_Signal"] = df["Close"] < df["Low_20"]

# Plot the data
plt.plot(df["Close"], label="Stock Price")
plt.plot(df["High_20"], label="20-Day High", linestyle="dashed")
plt.plot(df["Low_20"], label="20-Day Low", linestyle="dashed")
plt.legend()
plt.show()
</syntaxhighlight>
==Advantages==
*'''Captures Momentum'''
**Takes advantage of strong directional moves.
*'''Works in Trending Markets'''
**Most effective when strong trends follow breakouts.
*'''Can Be Automated'''
**Easily implemented in algorithmic trading systems.
==Limitations==
*'''False Breakouts'''
**Prices may reverse quickly after breaking out.
*'''Sideways Markets'''
**The system may generate frequent stop-outs in choppy markets.
*'''Requires Stop-Loss Discipline'''
**Prevents large losses from false breakouts.
==Applications==
*'''Stock Trading'''
**Used for breakout strategies in equities.
*'''Forex Trading'''
**Commonly applied in currency markets for range breakouts.
*'''Commodity Markets'''
**Effective in commodities that experience trend breakouts.
==See Also==
*[[Breakout Trading]]
*[[Support and Resistance]]
*[[Trend Following Strategy]]
*[[Volatility-Based Trading]]
*[[Technical Analysis]]

1-200 Moving Average System

2025-02-25T21:01:11Z

Slack:

'''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==
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.
==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==
A simple implementation of the 1-200 moving average system in Python:<syntaxhighlight lang="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()
</syntaxhighlight>
==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==
* '''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==
*'''Stock Trading'''**Commonly used in equity markets.
*'''Forex Trading'''
**Helps traders identify major currency trends.
*'''Cryptocurrency'''
**Used to navigate volatile crypto markets.
==See Also==
*[[Moving Average]]
* [[Golden Cross]]
*[[Death Cross]]
*[[Trend Following Strategy]]
*[[Technical Analysis]]

1-200 Moving Average System

2025-02-25T20:59:26Z

Slack: 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..."

'''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.
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==
#<nowiki>**Buy Signal** – When the price (1MA) crosses above the 200MA, indicating an uptrend (Golden Cross).</nowiki>
#<nowiki>**Sell Signal** – When the price (1MA) crosses below the 200MA, indicating a downtrend (Death Cross).</nowiki>
==Example==
A simple implementation of the 1-200 moving average system in Python:<syntaxhighlight lang="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()
</syntaxhighlight>
==Advantages==
*<nowiki>**Simple to Implement** – Easy to calculate and interpret.</nowiki>
*<nowiki>**Effective for Trend Following** – Helps capture long-term trends.</nowiki>
*<nowiki>**Reduces Market Noise** – Filters out short-term fluctuations.</nowiki>
==Limitations==
*<nowiki>**Lagging Indicator** – Signals appear after trends have started.</nowiki>
*<nowiki>**Whipsaw Risk** – Frequent false signals in sideways markets.</nowiki>
*<nowiki>**Not Suitable for Short-Term Trading** – Designed for long-term trend identification.</nowiki>
==Applications==
*<nowiki>**Stock Trading** – Commonly used in equity markets.</nowiki>
*<nowiki>**Forex Trading** – Helps traders identify major currency trends.</nowiki>
*<nowiki>**Cryptocurrency** – Used to navigate volatile crypto markets.</nowiki>
==See Also==
*[[Moving Average]]
*[[Golden Cross]]
*[[Death Cross]]
*[[Trend Following Strategy]]
*[[Technical Analysis]]

Hindsight Bias

2025-02-25T20:54:21Z

Slack: Created page with "'''Hindsight Bias''' is a cognitive bias that leads people to perceive past events as having been more predictable than they actually were. This bias distorts memory and judgment, making individuals believe they "knew it all along" after an event has occurred. ==Definition== Hindsight bias occurs when people: *Overestimate their ability to predict an outcome after knowing what happened. *View past events as more obvious than they were at the time. *Misremember their prev..."

'''Hindsight Bias''' is a cognitive bias that leads people to perceive past events as having been more predictable than they actually were. This bias distorts memory and judgment, making individuals believe they "knew it all along" after an event has occurred.
==Definition==
Hindsight bias occurs when people:
*Overestimate their ability to predict an outcome after knowing what happened.
*View past events as more obvious than they were at the time.
*Misremember their previous predictions as being more accurate.
==Causes==
Hindsight bias is influenced by:
*'''Memory Distortion''' – People reconstruct past beliefs to align with known outcomes.
*'''Inevitability Perception''' – Events seem more deterministic in hindsight.
*'''Cognitive Dissonance Reduction''' – Aligning past beliefs with outcomes reduces psychological discomfort.
==Example==
A common example of hindsight bias:
*Before an election: "The race is too close to call."
*After the election: "It was obvious that the winner would win all along."
==Effects==
Hindsight bias can lead to:
*'''Overconfidence''' – Believing one has better predictive abilities than reality.
*'''Blaming Others Unfairly''' – Judging decisions more harshly after knowing the result.
*'''Poor Decision-Making''' – Ignoring genuine uncertainty in future planning.
==Applications==
Hindsight bias affects various fields:
*'''Investing''' – Investors may believe they predicted market trends when, in reality, outcomes were uncertain.
*'''Medicine''' – Diagnoses may seem obvious in retrospect, leading to unfair assessments of past decisions.
*'''Legal Judgments''' – Jurors may see accidents as preventable after knowing the consequences.
==Prevention Strategies==
*Keeping records of predictions to compare with actual outcomes.
*Considering alternative possibilities before evaluating past events.
*Using data-driven decision-making instead of relying on memory.
==See Also==
*[[Cognitive Bias]]
*[[Overconfidence Effect]]
*[[Confirmation Bias]]
*[[Memory Distortion]]
*[[Decision-Making Psychology]]

Trend Following Strategy

2025-02-25T20:49:18Z

Slack: Created page with "'''Trend Following Strategy''' is a trading approach that seeks to capitalize on market trends by buying assets in an uptrend and selling (or shorting) assets in a downtrend. It is widely used in stocks, commodities, forex, and futures markets. ==Concept== Trend following strategies operate on the principle that markets tend to move in sustained trends rather than random fluctuations. Traders using this approach do not attempt to predict price movements but instead react..."

'''Trend Following Strategy''' is a trading approach that seeks to capitalize on market trends by buying assets in an uptrend and selling (or shorting) assets in a downtrend. It is widely used in stocks, commodities, forex, and futures markets.
==Concept==
Trend following strategies operate on the principle that markets tend to move in sustained trends rather than random fluctuations. Traders using this approach do not attempt to predict price movements but instead react to existing trends.
==Characteristics==
*'''Price-Based Strategy''' – Relies on market price action rather than fundamental analysis.
*'''Medium to Long-Term Approach''' – Trends can last from weeks to months or even years.
*'''Rules-Based Trading''' – Uses predefined entry and exit criteria.
*'''Risk Management Focus''' – Uses stop-loss orders and position sizing to manage risk.
==Common Trend Following Indicators==
Trend followers use technical indicators to identify and confirm trends:
*'''Moving Averages'''
**Simple Moving Average (SMA) and Exponential Moving Average (EMA) to smooth price data.
*'''Bollinger Bands'''
**Measures volatility to identify potential trend continuations or reversals.
*'''Average Directional Index (ADX)'''
**Quantifies trend strength.
*'''Breakout Trading'''
**Trades based on price breaking above or below key levels.
==Example Strategy: Moving Average Crossover==
One common trend-following method is the '''moving average crossover strategy''', which involves:
#Using two moving averages – A short-term moving average (e.g., 50-day SMA) and a long-term moving average (e.g., 200-day SMA).
#Buy signal – When the short-term moving average crosses above the long-term moving average (Golden Cross).
#Sell signal – When the short-term moving average crosses below the long-term moving average (Death Cross).
===Example Implementation in Python===
<syntaxhighlight lang="python">
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Sample Data: Load historical stock prices
df = pd.read_csv("stock_prices.csv")
df["50_SMA"] = df["Close"].rolling(window=50).mean()
df["200_SMA"] = df["Close"].rolling(window=200).mean()

# Identify buy and sell signals
df["Signal"] = np.where(df["50_SMA"] > df["200_SMA"], 1, 0)

plt.plot(df["Close"], label="Stock Price")
plt.plot(df["50_SMA"], label="50-day SMA")
plt.plot(df["200_SMA"], label="200-day SMA")
plt.legend()
plt.show()
</syntaxhighlight>
==Advantages==
*'''Captures Large Market Moves'''
**Profits from long-term trends.
*'''No Need to Predict Markets'''
**Reacts to price movements rather than forecasts.
*'''Works Across Multiple Asset Classes'''
**Can be applied to stocks, commodities, forex, and cryptocurrencies.
==Limitations==
*'''Whipsaw Risk'''
**False signals in choppy or sideways markets.
*'''Lagging Indicator'''
**Trend following reacts to established trends rather than predicting reversals.
*'''Drawdowns'''
**Trend followers can experience prolonged losses during trendless periods.
==Applications==
*'''Systematic Trading Funds'''
**Used by hedge funds and CTAs (Commodity Trading Advisors).
*'''Algorithmic Trading'''
**Implemented in automated trading systems.
*'''Portfolio Diversification'''
**Reduces risk by capturing trends in multiple asset classes.
==See Also==
*[[Momentum Trading]]
*[[Moving Average]]
*[[Technical Analysis]]
*[[Algorithmic Trading]]
*[[Risk Management]]

Behind the Eight Ball

2025-02-25T20:48:58Z

Slack: Created page with "'''Behind the Eight Ball''' is an idiomatic expression meaning to be in a difficult, disadvantageous, or losing situation. The phrase originates from the game of pool, where being positioned behind the 8-ball can leave a player without a clear shot. ==Origin== The expression comes from '''Kelly Pool''', a variation of pocket billiards. In this game: *Players are assigned specific balls. *The 8-ball is often an obstacle. *If a player's cue ball is positioned behind the 8-..."

'''Behind the Eight Ball''' is an idiomatic expression meaning to be in a difficult, disadvantageous, or losing situation. The phrase originates from the game of pool, where being positioned behind the 8-ball can leave a player without a clear shot.
==Origin==
The expression comes from '''Kelly Pool''', a variation of pocket billiards. In this game:
*Players are assigned specific balls.
*The 8-ball is often an obstacle.
*If a player's cue ball is positioned behind the 8-ball, it can block a clear shot, putting the player at a disadvantage.
The term later evolved into a metaphor for being in a tough position in various aspects of life.
==Usage==
The phrase is commonly used in everyday language to describe:
*Being in a difficult situation – "After missing the deadline, he was really behind the eight ball."
*Having a disadvantage in a competition – "Our team was behind the eight ball after losing the first two rounds."
*Facing financial difficulties – "After losing his job, he found himself behind the eight ball with bills piling up."
==Cultural References==
*Books and film – The phrase has appeared in literature and movies, often symbolizing a character's struggle.
*Music – "Behind the 8 Ball" is a song by several artists referencing hardship.
*Business and politics – Used to describe challenging circumstances in decision-making and strategy.
==See Also==
*[[Idioms]]
*[[Kelly Pool]]
*[[Billiards Terminology]]
*[[Metaphors in Language]]