Quantitative model

1. Find Pivot Point and Price Channel

Pivot points are crucial price levels calculated based on the high, low, and close prices of the previous trading session. They are used to identify potential support and resistance levels.

A stock channel, or price channel, is a price range within which a stock fluctuates over a specific period. Price channels help determine the current market trend. There are three common types of price channels:

• Ascending channel: Prices move within an uptrend with successively higher lows and highs.

• Descending channel: Prices move within a downtrend with successively lower lows and highs.

• Sideways channel: Prices fluctuate within a narrow range, without a clear trend.

A price channel is defined by two trendlines:

• The upper trendline connects the highs.

• The lower trendline connects the lows.

When the price touches the upper or lower trendline, it can react in two ways:

• Bounce back if the price channel continues to hold.

• Break out if the trend changes, potentially leading to the formation of a new price channel.

Stock channels are often used to find entry points to buy in support zones and sell in resistance zones.

Pivot Point (purple dots) in VCB's historical price data.

Retrieve Historical Data from the FiinQuant Library

from FiinQuantX import FiinSession
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from scipy import stats
username = 'YOUR_USERNAME'
password = 'YOUR PASWORD'
client = FiinSession(
    username=username,
    password=password
).login()
 

data = client.Fetch_Trading_Data(
    tickers='VCB',
    fields=['open', 'high', 'low', 'close', 'volume'],
    adjusted=True,
    period=1000,
    realtime=False,
    by='1d',
).get_data()

Find Pivot Point and plot stock prices

# Function to identify pivot points in price data
# Returns: 0 for no pivot, 1 for pivot high, 2 for pivot low, 3 for both
# Parameters:
#   candle: index of the current candle being checked
#   window: number of candles to check on either side
def isPivot(candle, window):
    if candle-window < 0 or candle+window >= len(data):
        return 0
    
    pivotHigh = 1
    pivotLow = 2
    for i in range(candle-window, candle+window+1):
        if data.iloc[candle].Low > data.iloc[i].Low:
            pivotLow=0
        if data.iloc[candle].High < data.iloc[i].High:
            pivotHigh=0
    if (pivotHigh and pivotLow):
        return 3
    elif pivotHigh:
        return pivotHigh
    elif pivotLow:
        return pivotLow
    else:
        return 0

# Apply isPivot function to each row of data
window=10
data['isPivot'] = data.apply(lambda x: isPivot(x.name,window), axis=1)

# Function to determine plotting position for pivot points
# Returns slightly offset values for visual clarity on the chart
# Parameters:
#   x: row of dataframe containing price and pivot information
def pointpos(x):
    if x['isPivot']==2:
        return x['Low']-1e-3  # Offset below pivot low
    elif x['isPivot']==1:
        return x['High']+1e-3 # Offset above pivot high
    else:
        return np.nan

# Apply pointpos function to create plotting positions
data['pointpos'] = data.apply(lambda row: pointpos(row), axis=1)


# Create candlestick chart
dfpl = data.copy()
fig = 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=5, color="MediumPurple"),
                name="pivot")
                
fig.update_layout(xaxis_rangeslider_visible=False)

fig.show()

Find Price Channel based on Pivot Point

Sideway Channel

# Function to identify and calculate channel slopes using pivot points
# Parameters:
#   candle: Current candle position to analyze from
#   backcandles: Number of previous candles to look back
#   window: Window size for pivot point calculation
def collect_channel(candle, backcandles, window):
    # Get subset of data for analysis
    localdf = data[candle-backcandles-window:candle-window]
    localdf['isPivot'] = localdf.apply(lambda x: isPivot(x.name,window), axis=1)
    
    # Extract pivot highs and lows with their indices
    highs = localdf[localdf['isPivot']==1].High.values
    idxhighs = localdf[localdf['isPivot']==1].High.index
    lows = localdf[localdf['isPivot']==2].Low.values
    idxlows = localdf[localdf['isPivot']==2].Low.index
    
    # Calculate regression lines if enough pivot points exist
    if len(lows)>=2 and len(highs)>=2:
        # Calculate regression for lower channel (pivot lows)
        sl_lows, interc_lows, r_value_l, _, _ = stats.linregress(idxlows,lows)
        # Calculate regression for upper channel (pivot highs)
        sl_highs, interc_highs, r_value_h, _, _ = stats.linregress(idxhighs,highs)
    
        return(sl_lows, interc_lows, sl_highs, interc_highs, r_value_l**2, r_value_h**2)
    else:
        return(0,0,0,0,0,0)

# Set parameters for channel analysis
candle = 200          # Current candle position
backcandles = 100     # Number of candles to look back
window = 3            # Window size for pivot calculation

# Create candlestick chart
fig = go.Figure(data=[go.Candlestick(x=dfpl.index,
                open=dfpl['open'],
                high=dfpl['high'],
                low=dfpl['low'],
                close=dfpl['close'])])

# Add pivot points to chart
fig.add_scatter(x=dfpl.index, y=dfpl['pointpos'], mode="markers",
                marker=dict(size=5, color="MediumPurple"),
                name="pivot")

# Calculate channel slopes and angles
sl_lows, interc_lows, sl_highs, interc_highs, r_sq_l, r_sq_h = collect_channel(candle, backcandles, window)
print(sl_lows,sl_highs)
angle_lows = np.degrees(np.arctan(sl_lows))
angle_highs = np.degrees(np.arctan(sl_highs))

# Plot channel lines
x = np.array(range(candle-backcandles-window, candle+1))
fig.add_trace(go.Scatter(x=x, y=sl_lows*x + interc_lows, mode='lines', name='lower slope'))
fig.add_trace(go.Scatter(x=x, y=sl_highs*x + interc_highs, mode='lines', name='max slope'))
fig.update_layout(xaxis_rangeslider_visible=False)
fig.show()