SuperTrend, Open Interest, and RSI. Each of these indicators can provide valuable insights when used in technical analysis for trading. Here’s a brief overview:
- SuperTrend:
- A trend-following indicator that can help identify the general market direction and potential buy or sell signals. It typically consists of a “trend line” overlaid on the price chart and changes direction when a certain threshold is reached.
- Formula: SuperTrend is usually calculated based on the Average True Range (ATR). The basic idea is to compute a baseline and adjust it with a multiple of the ATR.
def calculate_supertrend(df, period=7, multiplier=3):
df[‘ATR’] = df[‘TrueRange’].rolling(window=period).mean()
df[‘UpperBand’] = ((df[‘High’] + df[‘Low’]) / 2) + (multiplier * df[‘ATR’])
df[‘LowerBand’] = ((df[‘High’] + df[‘Low’]) / 2) – (multiplier * df[‘ATR’])df['SuperTrend'] = np.where(df['Close'] > df['UpperBand'], df['LowerBand'], df['UpperBand']) return df
Open Interest:
- Refers to the total number of open contracts for a particular futures or options market. It can help traders understand the strength of a trend, as increasing open interest can indicate that new money is flowing into the market, while decreasing open interest may indicate profit-taking or trend reversal.
Relative Strength Index (RSI):
- A momentum oscillator that measures the speed and change of price movements. The RSI oscillates between 0 and 100, typically using 14 periods. Readings above 70 are considered overbought, and readings below 30 are considered oversold.
- Formula: RSI = 100 – [100 / (1 + RS)], where RS is the average gain of the up periods during the specified time frame divided by the average loss.
def calculate_rsi(df, period=14):
delta = df[‘Close’].diff(1)
gain = np.where(delta > 0, delta, 0)
loss = np.where(delta < 0, -delta, 0)avg_gain = pd.Series(gain).rolling(window=period).mean() avg_loss = pd.Series(loss).rolling(window=period).mean() rs = avg_gain / avg_loss rsi = 100 - (100 / (1 + rs)) df['RSI'] = rsi return df
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