Equity Technical Analysis — วิเคราะห์หุ้นด้วย

Equity Technical Analysis

Technical Analysis วิเคราะห์ทางเทคนิค ราคา Volume Chart Pattern Indicators Moving Average RSI MACD Bollinger Bands Candlestick หุ้น Forex Crypto

Technical Indicators Python

# technical_analysis.py — Technical Indicators

from dataclasses import dataclass

from typing import List, Optional

import math



@dataclass

class OHLCV:

    date: str

    open: float

    high: float

    low: float

    close: float

    volume: int



def sma(prices: List[float], period: int) -> List[Optional[float]]:

    """Simple Moving Average"""

    result = []

    for i in range(len(prices)):

        if i < period - 1:

            result.append(None)

        else:

            avg = sum(prices[i-period+1:i+1]) / period

            result.append(round(avg, 2))

    return result



def ema(prices: List[float], period: int) -> List[Optional[float]]:

    """Exponential Moving Average"""

    result = []

    multiplier = 2 / (period + 1)

    for i in range(len(prices)):

        if i < period - 1:

            result.append(None)

        elif i == period - 1:

            avg = sum(prices[:period]) / period

            result.append(round(avg, 2))

        else:

            prev = result[-1]

            val = (prices[i] - prev) * multiplier + prev

            result.append(round(val, 2))

    return result



def rsi(prices: List[float], period: int = 14) -> List[Optional[float]]:

    """Relative Strength Index"""

    result = [None] * period

    gains = []

    losses = []

    for i in range(1, len(prices)):

        change = prices[i] - prices[i-1]

        gains.append(max(change, 0))

        losses.append(max(-change, 0))



    if len(gains) < period:

        return [None] * len(prices)



    avg_gain = sum(gains[:period]) / period

    avg_loss = sum(losses[:period]) / period



    if avg_loss == 0:

        result.append(100)

    else:

        rs = avg_gain / avg_loss

        result.append(round(100 - (100 / (1 + rs)), 2))



    for i in range(period, len(gains)):

        avg_gain = (avg_gain * (period - 1) + gains[i]) / period

        avg_loss = (avg_loss * (period - 1) + losses[i]) / period

        if avg_loss == 0:

            result.append(100)

        else:

            rs = avg_gain / avg_loss

            result.append(round(100 - (100 / (1 + rs)), 2))

    return result



# ตัวอย่างข้อมูล

data = [

    OHLCV("2024-01-01", 100, 105, 98, 103, 1500000),

    OHLCV("2024-01-02", 103, 108, 101, 107, 1800000),

    OHLCV("2024-01-03", 107, 112, 105, 110, 2000000),

    OHLCV("2024-01-04", 110, 115, 108, 113, 1700000),

    OHLCV("2024-01-05", 113, 118, 111, 116, 2200000),

    OHLCV("2024-01-06", 116, 120, 114, 118, 1900000),

    OHLCV("2024-01-07", 118, 121, 115, 117, 1600000),

    OHLCV("2024-01-08", 117, 119, 112, 114, 2100000),

    OHLCV("2024-01-09", 114, 116, 110, 112, 1800000),

    OHLCV("2024-01-10", 112, 115, 109, 113, 1500000),

]



closes = [d.close for d in data]

sma5 = sma(closes, 5)

ema5 = ema(closes, 5)



print("=== Technical Analysis ===")

print(f"{'Date':<12} {'Close':>6} {'SMA5':>6} {'EMA5':>6}")

for i, d in enumerate(data):

    s = f"{sma5[i]}" if sma5[i] else "-"

    e = f"{ema5[i]}" if ema5[i] else "-"

    print(f"  {d.date:<12} {d.close:>6.1f} {s:>6} {e:>6}")

MACD และ Trading Signal

# macd.py — MACD & Trading Signals

def calc_macd(prices: List[float], fast: int = 12, slow: int = 26,

              signal: int = 9):

    """MACD (Moving Average Convergence Divergence)"""

    ema_fast = ema(prices, fast)

    ema_slow = ema(prices, slow)



    macd_line = []

    for i in range(len(prices)):

        if ema_fast[i] is not None and ema_slow[i] is not None:

            macd_line.append(round(ema_fast[i] - ema_slow[i], 2))

        else:

            macd_line.append(None)



    valid_macd = [m for m in macd_line if m is not None]

    signal_line = ema(valid_macd, signal)



    return macd_line, signal_line



# Trading Strategy

from dataclasses import dataclass



@dataclass

class Signal:

    date: str

    type: str  # BUY / SELL

    price: float

    indicator: str

    strength: str  # strong / moderate / weak



def generate_signals(data: List[OHLCV]) -> List[Signal]:

    signals = []

    closes = [d.close for d in data]

    rsi_values = rsi(closes, 5)  # ใช้ period 5 สำหรับ demo

    sma_short = sma(closes, 3)

    sma_long = sma(closes, 5)



    for i in range(1, len(data)):

        # RSI Signal

        if rsi_values[i] is not None:

            if rsi_values[i] < 30:

                signals.append(Signal(data[i].date, "BUY", data[i].close,

                                      "RSI Oversold", "strong"))

            elif rsi_values[i] > 70:

                signals.append(Signal(data[i].date, "SELL", data[i].close,

                                      "RSI Overbought", "strong"))



        # MA Crossover

        if (sma_short[i] is not None and sma_long[i] is not None and

            sma_short[i-1] is not None and sma_long[i-1] is not None):

            if sma_short[i-1] < sma_long[i-1] and sma_short[i] > sma_long[i]:

                signals.append(Signal(data[i].date, "BUY", data[i].close,

                                      "Golden Cross", "moderate"))

            elif sma_short[i-1] > sma_long[i-1] and sma_short[i] < sma_long[i]:

                signals.append(Signal(data[i].date, "SELL", data[i].close,

                                      "Death Cross", "moderate"))



    return signals



signals = generate_signals(data)

print("\n=== Trading Signals ===")

for s in signals:

    print(f"  [{s.type}] {s.date} @ {s.price} — {s.indicator} ({s.strength})")



# Candlestick Patterns

patterns = {

    "Hammer": {"type": "Bullish Reversal", "desc": "ร่างเล็ก ไส้ล่างยาว 2x ท้าย Downtrend"},

    "Shooting Star": {"type": "Bearish Reversal", "desc": "ร่างเล็ก ไส้บนยาว 2x ท้าย Uptrend"},

    "Engulfing": {"type": "Reversal", "desc": "แท่งที่ 2 กลืนแท่งที่ 1 ทั้งหมด"},

    "Doji": {"type": "Indecision", "desc": "Open = Close ตลาดลังเล"},

    "Morning Star": {"type": "Bullish Reversal", "desc": "3 แท่ง ลง-เล็ก-ขึ้น ท้าย Downtrend"},

    "Evening Star": {"type": "Bearish Reversal", "desc": "3 แท่ง ขึ้น-เล็ก-ลง ท้าย Uptrend"},

}



print(f"\n\n=== Candlestick Patterns ===")

for name, info in patterns.items():

    print(f"  [{name}] {info['type']}")

    print(f"    {info['desc']}")

Backtesting

# backtest.py — Simple Backtesting

@dataclass

class Trade:

    entry_date: str

    entry_price: float

    exit_date: str

    exit_price: float

    direction: str

    pnl: float

    pnl_pct: float



@dataclass

class BacktestResult:

    trades: List[Trade]

    total_trades: int

    winners: int

    losers: int

    win_rate: float

    total_pnl: float

    max_drawdown: float



def simple_backtest(data: List[OHLCV], initial_capital: float = 100000):

    trades = []

    position = None

    capital = initial_capital

    peak = initial_capital

    max_dd = 0



    closes = [d.close for d in data]

    sma_s = sma(closes, 3)

    sma_l = sma(closes, 5)



    for i in range(1, len(data)):

        if sma_s[i] is None or sma_l[i] is None:

            continue

        if sma_s[i-1] is None or sma_l[i-1] is None:

            continue



        # Buy Signal

        if position is None and sma_s[i-1] < sma_l[i-1] and sma_s[i] > sma_l[i]:

            position = {"date": data[i].date, "price": data[i].close}



        # Sell Signal

        elif position and sma_s[i-1] > sma_l[i-1] and sma_s[i] < sma_l[i]:

            pnl = data[i].close - position["price"]

            pnl_pct = (pnl / position["price"]) * 100

            trades.append(Trade(

                position["date"], position["price"],

                data[i].date, data[i].close,

                "LONG", round(pnl, 2), round(pnl_pct, 2)))

            capital += pnl * (capital / position["price"])

            position = None



        peak = max(peak, capital)

        dd = (peak - capital) / peak * 100

        max_dd = max(max_dd, dd)



    winners = [t for t in trades if t.pnl > 0]

    losers = [t for t in trades if t.pnl <= 0]

    win_rate = len(winners) / len(trades) * 100 if trades else 0



    return BacktestResult(

        trades=trades,

        total_trades=len(trades),

        winners=len(winners),

        losers=len(losers),

        win_rate=round(win_rate, 1),

        total_pnl=round(sum(t.pnl for t in trades), 2),

        max_drawdown=round(max_dd, 2),

    )



result = simple_backtest(data)

print("=== Backtest Results ===")

print(f"  Total Trades: {result.total_trades}")

print(f"  Win Rate: {result.win_rate}%")

print(f"  Total PnL: {result.total_pnl}")

print(f"  Max Drawdown: {result.max_drawdown}%")



for t in result.trades:

    status = "WIN" if t.pnl > 0 else "LOSS"

    print(f"  [{status}] {t.entry_date} @ {t.entry_price} -> "

          f"{t.exit_date} @ {t.exit_price} = {t.pnl_pct:+.1f}%")

เคล็ดลับ

• Confluence: ใช้ 2-3 Indicators ยืนยันกัน ไม่ใช้ตัวเดียว
• Risk: ไม่เกิน 1-2% ต่อ Trade ตั้ง Stop Loss ทุกครั้ง
• Timeframe: ใช้ Timeframe สูงยืนยัน Timeframe ต่ำ
• Journal: จด Trading Journal ทบทวนทุกสัปดาห์
• Backtest: ทดสอบ Strategy กับข้อมูลย้อนหลังก่อนใช้เงินจริง

การนำความรู้ไปประยุกต์ใช้งานจริง

แหล่งเรียนรู้ที่แนะนำ ได้แก่ Official Documentation ที่อัพเดทล่าสุดเสมอ Online Course จาก Coursera Udemy edX ช่อง YouTube คุณภาพทั้งไทยและอังกฤษ และ Community อย่าง Discord Reddit Stack Overflow ที่ช่วยแลกเปลี่ยนประสบการณ์กับนักพัฒนาทั่วโลก

เนื้อหาเกี่ยวข้อง — อ่านต่อ: กราฟ Day Trade — ข้อมูลครบถ้วน 2026

เปรียบเทียบข้อดีและข้อเสีย

จากตารางเปรียบเทียบจะเห็นว่าข้อดีมีมากกว่าข้อเสียอย่างชัดเจน โดยเฉพาะในแง่ของประสิทธิภาพและความสามารถในการ Scale สำหรับข้อเสียส่วนใหญ่สามารถแก้ไขได้ด้วยการเรียนรู้อย่างเป็นระบบและวางแผนทรัพยากรให้เหมาะสม

สรุป

Technical Analysis วิเคราะห์ทางเทคนิค Moving Average RSI MACD Bollinger Bands Candlestick Pattern Golden Cross Death Cross Backtest Risk Management Trading Journal

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