Once support/resistance trends are validated, the next step is to incorporate RSI to fine-tune trading signals. A unified approach helps identify optimal buy/sell moments.
Code Example:
def generateSignal(l, df, rsi_lower, rsi_upper, r_level, s_level):trend = confirmTrend(l, df, r_level, s_level)rsi_value = df[‘RSI’][l]
if trend == “below_support” and rsi_value < rsi_lower:return “buy”if trend == “above_resistance” and rsi_value > rsi_upper:return “sell”return “hold”
Detailed Explanation:
Inputs:l: Candle index for analysis.df: DataFrame containing RSI and market data.rsi_lower: RSI threshold for oversold conditions (default often set around 30).rsi_upper: RSI threshold for overbought conditions (default often set around 70).r_level: Resistance level.s_level: Support level.
2. Logic Flow:
Determines the trend using the confirmTrend() function.Checks the current RSI value for overbought or oversold conditions:If the price is below support and RSI indicates oversold, the signal is “buy”.If the price is above resistance and RSI shows overbought, the signal is “sell”.Otherwise, the signal remains “hold”.
3. Outputs:
Returns one of three trading signals:”buy”: Suggests entering a long position.”sell”: Suggests entering a short position.”hold”: Advises waiting for clearer opportunities.
Apply the support and resistance detection framework to identify actionable trading signals.
Code Implementation:
from tqdm import tqdm
n1, n2, backCandles = 8, 6, 140signal = [0] * len(df)
for row in tqdm(range(backCandles + n1, len(df) – n2)):signal[row] = check_candle_signal(row, n1, n2, backCandles, df)df[“signal”] = signal
Explanation:
Key Parameters:n1 = 8, n2 = 6: Reference candles before and after each potential support/resistance point.backCandles = 140: History used for analysis.
2. Signal Initialization:
signal = [0] * len(df): Prepare for tracking identified trading signals.
3. Using tqdm Loop:
Iterates across viable rows while displaying progress for large datasets.
4. Call to Detection Logic:
The check_candle_signal integrates RSI dynamics and proximity validation.
5. Updating Signals in Data:
Add results into a signal column for post-processing.
Visualize market movements by mapping precise trading actions directly onto price charts.
Code Implementation:
import numpy as np
def pointpos(x):if x[‘signal’] == 1:return x[‘high’] + 0.0001elif x[‘signal’] == 2:return x[‘low’] – 0.0001else:return np.nan
df[‘pointpos’] = df.apply(lambda row: pointpos(row), axis=1)
Breakdown:
Logic Behind pointpos:Ensures buy signals (1) sit slightly above high prices.Ensures sell signals (2) sit slightly below low prices.Returns NaN if signals are absent.
2. Dynamic Point Generation:
Applies point positions across rows, overlaying signals in visualizations.
Create comprehensive overlays of detected signals atop candlestick plots for better interpretability.
Code Implementation:
import plotly.graph_objects as go
dfpl = df[100:300] # Focused segmentfig = go.Figure(data=[go.Candlestick(x=dfpl.index,open=dfpl[‘open’],high=dfpl[‘high’],low=dfpl[‘low’],close=dfpl[‘close’])])fig.add_scatter(x=dfpl.index, y=dfpl[‘pointpos’],mode=’markers’, marker=dict(size=8, color=’MediumPurple’))fig.update_layout(width=1000, height=800, paper_bgcolor=’black’, plot_bgcolor=’black’)fig.show()
Insight:
Combines candlestick data with signal scatter annotations.Facilitates immediate recognition of actionable zones.
Enrich visual plots with horizontal demarcations for enhanced contextuality.
Code Implementation:
from plotly.subplots import make_subplots# Extended checkfig.add_shape(type=”line”, x0=10, …) # Stub logic for signal-resistance pair representation
Enhancing the strategy further, we visualize the detected support and resistance levels alongside the trading signals on the price chart.
Code Implementation:
def plot_support_resistance(df, backCandles, proximity):import plotly.graph_objects as go
# Extract a segment of the DataFrame for visualizationdf_plot = df[-backCandles:]
fig = go.Figure(data=[go.Candlestick(x=df_plot.index,open=df_plot[‘open’],high=df_plot[‘high’],low=df_plot[‘low’],close=df_plot[‘close’])])
# Add detected support levels as horizontal linesfor i, level in enumerate(df_plot[‘support’].dropna().unique()):fig.add_hline(y=level, line=dict(color=”MediumPurple”, dash=’dash’), name=f”Support {i}”)
# Add detected resistance levels as horizontal linesfor i, level in enumerate(df_plot[‘resistance’].dropna().unique()):fig.add_hline(y=level, line=dict(color=”Crimson”, dash=’dash’), name=f”Resistance {i}”)
fig.update_layout(title=”Support and Resistance Levels with Price Action”,autosize=True,width=1000,height=800,)fig.show()
Highlights:
Horizontal Support & Resistance Lines:support levels are displayed in purple dashes for clarity.resistance levels use red dashes to signify obstacles above the price.
2. Candlestick Chart:
Depicts open, high, low, and close prices for each candle.
3. Dynamic Updates:
Automatically adjusts based on selected data ranges (backCandles).
