Lære Candlestick Charts for Traders | Visualizing Market Trends and Indicators

Candlestick charts are a foundational tool for traders, offering a compact way to view four crucial price points for each period: open, high, low, and close (OHLC). Each candlestick encodes a story about market sentiment within a chosen time frame. The open is the first traded price, the close is the last, the high is the maximum price reached, and the low is the minimum. The body of the candlestick represents the range between open and close, while the thin lines, known as wicks or shadows, extend to the high and low. If the close is higher than the open, the candle is typically considered bullish, signaling buying pressure; if the close is lower, it is bearish, signaling selling pressure. This structure allows you to quickly assess whether buyers or sellers dominated the session, and how volatile the price action was.


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import matplotlib.pyplot as plt

# Sample OHLC data for 5 days
dates = ["Mon", "Tue", "Wed", "Thu", "Fri"]
opens = [100, 102, 101, 105, 103]
highs = [105, 106, 104, 108, 107]
lows = [99, 101, 100, 104, 102]
closes = [104, 103, 102, 106, 105]

fig, ax = plt.subplots(figsize=(7, 4))

for i in range(len(dates)):
    # Draw the wick
    ax.plot([i, i], [lows[i], highs[i]], color="black", linewidth=1)
    # Draw the body
    lower = min(opens[i], closes[i])
    height = abs(opens[i] - closes[i])
    rect = plt.Rectangle((i - 0.2, lower), 0.4, height if height > 0 else 0.1, 
                         edgecolor="black", facecolor="white", linewidth=1)
    ax.add_patch(rect)

ax.set_xticks(range(len(dates)))
ax.set_xticklabels(dates)
ax.set_ylabel("Price")
ax.set_title("Simple Candlestick Chart (5 Days)")
plt.show()

Being able to interpret candlestick charts is essential for recognizing changes in market sentiment. A bullish candle forms when the close is higher than the open, usually indicating that buyers have been in control. Conversely, a bearish candle appears when the close is below the open, suggesting that sellers have dominated. Traders often look for specific patterns, such as a series of bullish candles signaling upward momentum, or formations like doji (where open and close are nearly the same) that may indicate indecision. Patterns such as engulfing, hammer, or shooting star are watched closely, as they can signal potential reversals or continuations in price trends. Recognizing these patterns can help you anticipate shifts in direction and inform your trading decisions.


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import matplotlib.pyplot as plt

# Sample OHLC data for 5 days
dates = ["Mon", "Tue", "Wed", "Thu", "Fri"]
opens = [100, 102, 101, 105, 103]
highs = [105, 106, 104, 108, 107]
lows = [99, 101, 100, 104, 102]
closes = [104, 103, 102, 106, 105]

fig, ax = plt.subplots(figsize=(7, 4))

for i in range(len(dates)):
    # Determine candle color
    if closes[i] >= opens[i]:
        color = "green"  # Bullish
    else:
        color = "red"    # Bearish
    # Draw the wick
    ax.plot([i, i], [lows[i], highs[i]], color="black", linewidth=1)
    # Draw the body
    lower = min(opens[i], closes[i])
    height = abs(opens[i] - closes[i])
    rect = plt.Rectangle((i - 0.2, lower), 0.4, height if height > 0 else 0.1,
                         edgecolor="black", facecolor=color, linewidth=1)
    ax.add_patch(rect)

ax.set_xticks(range(len(dates)))
ax.set_xticklabels(dates)
ax.set_ylabel("Price")
ax.set_title("Candlestick Chart with Bullish/Bearish Colors")
plt.show()

1. What information does a candlestick chart provide that a simple line chart does not?

2. How can color coding enhance the interpretability of candlestick charts?

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Sektion 2. Kapitel 4

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              1234567891011121314151617181920212223242526
            
import matplotlib.pyplot as plt

# Sample OHLC data for 5 days
dates = ["Mon", "Tue", "Wed", "Thu", "Fri"]
opens = [100, 102, 101, 105, 103]
highs = [105, 106, 104, 108, 107]
lows = [99, 101, 100, 104, 102]
closes = [104, 103, 102, 106, 105]

fig, ax = plt.subplots(figsize=(7, 4))

for i in range(len(dates)):
    # Draw the wick
    ax.plot([i, i], [lows[i], highs[i]], color="black", linewidth=1)
    # Draw the body
    lower = min(opens[i], closes[i])
    height = abs(opens[i] - closes[i])
    rect = plt.Rectangle((i - 0.2, lower), 0.4, height if height > 0 else 0.1, 
                         edgecolor="black", facecolor="white", linewidth=1)
    ax.add_patch(rect)

ax.set_xticks(range(len(dates)))
ax.set_xticklabels(dates)
ax.set_ylabel("Price")
ax.set_title("Simple Candlestick Chart (5 Days)")
plt.show()


              12345678910111213141516171819202122232425262728293031
            
import matplotlib.pyplot as plt

# Sample OHLC data for 5 days
dates = ["Mon", "Tue", "Wed", "Thu", "Fri"]
opens = [100, 102, 101, 105, 103]
highs = [105, 106, 104, 108, 107]
lows = [99, 101, 100, 104, 102]
closes = [104, 103, 102, 106, 105]

fig, ax = plt.subplots(figsize=(7, 4))

for i in range(len(dates)):
    # Determine candle color
    if closes[i] >= opens[i]:
        color = "green"  # Bullish
    else:
        color = "red"    # Bearish
    # Draw the wick
    ax.plot([i, i], [lows[i], highs[i]], color="black", linewidth=1)
    # Draw the body
    lower = min(opens[i], closes[i])
    height = abs(opens[i] - closes[i])
    rect = plt.Rectangle((i - 0.2, lower), 0.4, height if height > 0 else 0.1,
                         edgecolor="black", facecolor=color, linewidth=1)
    ax.add_patch(rect)

ax.set_xticks(range(len(dates)))
ax.set_xticklabels(dates)
ax.set_ylabel("Price")
ax.set_title("Candlestick Chart with Bullish/Bearish Colors")
plt.show()

1. What information does a candlestick chart provide that a simple line chart does not?

2. How can color coding enhance the interpretability of candlestick charts?

Var alt klart?

Tak for dine kommentarer!

Sektion 2. Kapitel 4