Median Price (MEDPRICE)¶

The Median Price (MEDPRICE) is a price transformation that calculates the midpoint between the high and low prices for each period. Unlike the close price, which only captures the final transaction price, the median price represents the typical intraday midpoint, filtering out some noise while providing a balanced view of the trading range. It's commonly used as input to other technical indicators and for smoothing price data in analysis.

Usage¶

require 'sqa/tai'

high = [48.70, 48.72, 48.90, 48.87, 48.82]
low =  [47.79, 48.14, 48.39, 48.37, 48.24]

medprice = SQA::TAI.medprice(high, low)

puts "Current Median Price: #{medprice.last.round(2)}"

Parameters¶

Parameter	Type	Required	Default	Description
`high`	Array	Yes	-	Array of high prices for each period
`low`	Array	Yes	-	Array of low prices for each period

Returns¶

Returns an array of median price values, one for each period. No warmup period needed as this is a simple bar-by-bar calculation.

Formula¶

Median Price = (High + Low) / 2

This formula calculates the arithmetic mean of the highest and lowest prices traded during each period, representing the midpoint of the trading range.

Interpretation¶

Concept	Description
Typical Midpoint	Represents the average of extremes, smoothing intraday volatility
Range Center	Shows where price action centered during the period
Noise Filtering	Less affected by gap openings or volatile closes
Balance Point	Captures both buying and selling extremes equally

Note: Array elements should be ordered from oldest to newest (chronological order)

The median price is particularly useful when: - You want to reduce the impact of outlier closes - Analyzing the typical price level for a period - Building indicators that need smoothed price input - Comparing price action across different securities

Example: Basic MEDPRICE Calculation¶

high =      [48.70, 48.72, 48.90, 48.87, 48.82]
low =       [47.79, 48.14, 48.39, 48.37, 48.24]
close =     [48.20, 48.61, 48.75, 48.63, 48.74]

medprice = SQA::TAI.medprice(high, low)

# Compare median price to close
puts "Period Analysis:"
medprice.each_with_index do |mp, i|
  difference = close[i] - mp
  position = difference > 0 ? "above" : "below"

  puts "Period #{i+1}:"
  puts "  High: #{high[i]}, Low: #{low[i]}, Close: #{close[i]}"
  puts "  Median Price: #{mp.round(2)}"
  puts "  Close is #{difference.abs.round(2)} #{position} median"
  puts "  #{difference > 0 ? 'Bullish close (upper half)' : 'Bearish close (lower half)'}"
  puts
end

Example: MEDPRICE vs Close Price¶

high_prices =  load_historical_data('AAPL', field: :high)
low_prices =   load_historical_data('AAPL', field: :low)
close_prices = load_historical_data('AAPL', field: :close)

medprice = SQA::TAI.medprice(high_prices, low_prices)

# Analyze where closes fall relative to median
recent_periods = 20
upper_half_closes = 0
lower_half_closes = 0

recent_periods.times do |i|
  index = -(i + 1)
  if close_prices[index] > medprice[index]
    upper_half_closes += 1
  else
    lower_half_closes += 1
  end
end

puts "Last #{recent_periods} periods:"
puts "Closes in upper half: #{upper_half_closes} (#{(upper_half_closes.to_f/recent_periods*100).round(1)}%)"
puts "Closes in lower half: #{lower_half_closes} (#{(lower_half_closes.to_f/recent_periods*100).round(1)}%)"

if upper_half_closes > lower_half_closes * 1.5
  puts "Strong bullish bias - closes consistently in upper range"
elsif lower_half_closes > upper_half_closes * 1.5
  puts "Strong bearish bias - closes consistently in lower range"
else
  puts "Balanced - no clear directional bias"
end

Example: MEDPRICE as Moving Average Input¶

high_prices = load_historical_data('MSFT', field: :high)
low_prices =  load_historical_data('MSFT', field: :low)
close_prices = load_historical_data('MSFT', field: :close)

# Compare different moving average inputs
ma_close = SQA::TAI.sma(close_prices, period: 20)

medprice = SQA::TAI.medprice(high_prices, low_prices)
ma_median = SQA::TAI.sma(medprice, period: 20)

current_close = close_prices.last
current_ma_close = ma_close.last
current_ma_median = ma_median.last

puts "Moving Average Comparison (20-period):"
puts "Price: #{current_close.round(2)}"
puts "MA of Close: #{current_ma_close.round(2)}"
puts "MA of Median: #{current_ma_median.round(2)}"
puts "Difference: #{(current_ma_close - current_ma_median).round(2)}"

# Median-based MA is often smoother and less affected by gaps
if (current_close - current_ma_median).abs < (current_close - current_ma_close).abs
  puts "MA of Median Price provides tighter support/resistance"
end

Example: Support and Resistance Levels¶

high_prices = load_historical_data('TSLA', field: :high)
low_prices =  load_historical_data('TSLA', field: :low)

medprice = SQA::TAI.medprice(high_prices, low_prices)

# Find recent pivot points using median price
lookback = 50
recent_medians = medprice[-lookback..-1]

# Calculate potential support/resistance zones
resistance_zone = recent_medians.max
support_zone = recent_medians.min
current_median = medprice.last

puts "Support/Resistance Analysis (#{lookback} periods):"
puts "Resistance (highest median): #{resistance_zone.round(2)}"
puts "Current Median Price: #{current_median.round(2)}"
puts "Support (lowest median): #{support_zone.round(2)}"

range = resistance_zone - support_zone
current_position = ((current_median - support_zone) / range * 100).round(1)

puts "Current position in range: #{current_position}%"

case current_position
when 0...25
  puts "Near support - potential bounce zone"
when 25...50
  puts "Lower half of range"
when 50...75
  puts "Upper half of range"
when 75..100
  puts "Near resistance - potential reversal zone"
end

Example: Volume-Weighted Analysis¶

high_prices =   load_historical_data('SPY', field: :high)
low_prices =    load_historical_data('SPY', field: :low)
close_prices =  load_historical_data('SPY', field: :close)
volume =        load_historical_data('SPY', field: :volume)

medprice = SQA::TAI.medprice(high_prices, low_prices)

# Calculate volume-weighted median price
lookback = 20
total_volume = 0
weighted_sum = 0

lookback.times do |i|
  index = -(i + 1)
  total_volume += volume[index]
  weighted_sum += medprice[index] * volume[index]
end

vwmp = weighted_sum / total_volume  # Volume-Weighted Median Price

current_close = close_prices.last
puts "Volume-Weighted Analysis (#{lookback} periods):"
puts "Current Close: #{current_close.round(2)}"
puts "Volume-Weighted Median Price: #{vwmp.round(2)}"

if current_close > vwmp
  puts "Price above VWMP - bullish positioning"
  puts "Distance: +#{((current_close - vwmp) / vwmp * 100).round(2)}%"
else
  puts "Price below VWMP - bearish positioning"
  puts "Distance: -#{((vwmp - current_close) / vwmp * 100).round(2)}%"
end

When to Use MEDPRICE vs Close Price¶

Use Median Price When:¶

Reducing Gap Impact: Median price ignores opening gaps, focusing on intraday range
Smoothing Indicators: Building moving averages or oscillators with less noise
Range Analysis: Studying where price action centers within each period
Support/Resistance: Identifying typical price levels for pivot points
Volume Studies: Creating volume-weighted price measures
Commodity Trading: Common in commodity markets where range matters more
Intraday Analysis: Understanding typical price levels within bars

Use Close Price When:¶

Momentum Analysis: Close reflects final trader sentiment
Trend Following: Close shows direction of price resolution
Pattern Recognition: Most candlestick patterns rely on close
Gap Trading: Gaps are important signals in your strategy
End-of-Day Systems: Trading on daily close signals
Market Sentiment: Close shows who won the period (bulls or bears)
Standard Indicators: Most indicators expect close price input

Key Differences:¶

Aspect	Median Price	Close Price
Calculation	(High + Low) / 2	Last trade of period
Sensitivity	Less volatile	More volatile
Gap Impact	Not affected by gaps	Reflects gaps
Use Case	Smoothing, range analysis	Momentum, sentiment
Noise Level	Lower noise	Higher noise
Trading Style	Range trading, mean reversion	Trend following, breakouts

Example: Comparing Price Transforms¶

high_prices =  load_historical_data('NVDA', field: :high)
low_prices =   load_historical_data('NVDA', field: :low)
close_prices = load_historical_data('NVDA', field: :close)

# Calculate all price transforms
medprice = SQA::TAI.medprice(high_prices, low_prices)
typprice = SQA::TAI.typprice(high_prices, low_prices, close_prices)
wclprice = SQA::TAI.wclprice(high_prices, low_prices, close_prices)

current_close = close_prices.last
current_med = medprice.last
current_typ = typprice.last
current_wcl = wclprice.last

puts "Price Transform Comparison:"
puts "Close Price:    #{current_close.round(2)}"
puts "Median Price:   #{current_med.round(2)} (high+low)/2"
puts "Typical Price:  #{current_typ.round(2)} (high+low+close)/3"
puts "Weighted Close: #{current_wcl.round(2)} (high+low+2*close)/4"
puts

# Analyze spreads
med_spread = (current_close - current_med).abs
typ_spread = (current_close - current_typ).abs
wcl_spread = (current_close - current_wcl).abs

puts "Distance from Close:"
puts "Median:   #{med_spread.round(2)} (#{(med_spread/current_close*100).round(2)}%)"
puts "Typical:  #{typ_spread.round(2)} (#{(typ_spread/current_close*100).round(2)}%)"
puts "Weighted: #{wcl_spread.round(2)} (#{(wcl_spread/current_close*100).round(2)}%)"
puts

# MEDPRICE has largest deviation potential (ignores close)
# WCLPRICE has smallest deviation (weighted toward close)
if med_spread > typ_spread * 1.5
  puts "Large median deviation suggests close near high or low extreme"
  if current_close > current_med
    puts "Close near high - strong buying"
  else
    puts "Close near low - strong selling"
  end
end

Advanced Techniques¶

1. Median Price Channels¶

Create channels using median price moving averages: - Upper channel: MA(Median) + N * ATR - Lower channel: MA(Median) - N * ATR - Smoother than close-based channels

2. Mean Reversion Trading¶

Median price naturally mean-reverts within ranges: - Buy when close < median - threshold - Sell when close > median + threshold - Works well in ranging markets

3. Filtered Indicators¶

Use median price input for cleaner signals: - RSI on median price (less whipsaw) - MACD on median price (smoother crossovers) - Bollinger Bands on median price (better range definition)

4. Intraday Position Analysis¶

Track where closes fall relative to median: - Consistent upper-half closes = bullish - Consistent lower-half closes = bearish - Useful for confirming trend strength

Common Settings¶

MEDPRICE has no period parameter - it's calculated bar-by-bar. However, when used as input to other indicators:

Input Usage	Period	Use Case
SMA(MEDPRICE)	10-20	Short-term trend
SMA(MEDPRICE)	50	Intermediate trend
SMA(MEDPRICE)	200	Long-term trend
RSI(MEDPRICE)	14	Smoother momentum
BBANDS(MEDPRICE)	20	Range-based volatility

Advantages and Limitations¶

Advantages:¶

Simple, transparent calculation
Filters gap-related noise
Represents true intraday center
Works well for range-based strategies
Less affected by single-trade spikes
Good input for volume-weighted studies

Limitations:¶

Ignores opening price completely
Doesn't capture final sentiment (close)
Less useful for momentum strategies
May miss important closing patterns
Not standard input for most indicators
Less intuitive than close price

TYPPRICE - Typical Price (includes close)
WCLPRICE - Weighted Close Price (emphasizes close)
AVGPRICE - Average Price (includes open)
SMA - Often applied to median price
CCI - Uses typical price (similar concept)