Module: SqaDemo::Sinatra::Helpers::ApiHelpers

Defined in:
lib/sqa_demo/sinatra/helpers/api_helpers.rb

Instance Method Summary collapse

Instance Method Details

#analyze_fpop(stock, prices) ⇒ Object

Analyze FPOP (Future Period Loss/Profit)



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# File 'lib/sqa_demo/sinatra/helpers/api_helpers.rb', line 152

def analyze_fpop(stock, prices)
  dates = stock.df["timestamp"].to_a.map(&:to_s)
  last_date = Date.parse(dates.last)
  fpop_period = 10
  fpop_data = SQA::FPOP.fpl_analysis(prices, fpop: fpop_period)

  # Generate future trading dates (skip weekends)
  future_dates = []
  current_date = last_date + 1
  while future_dates.length < fpop_period
    unless current_date.saturday? || current_date.sunday?
      future_dates << current_date.to_s
    end
    current_date += 1
  end

  # Show last 5 historical predictions + 10 future predictions
  num_historical = [5, dates.length - 1].min
  recent_fpop = []

  # Historical predictions with verification
  hist_start = dates.length - num_historical - 1
  (0...num_historical).each do |i|
    idx = hist_start + i
    target_idx = idx + 1
    next if idx < 0 || idx >= fpop_data.length || target_idx >= prices.length

    prev_price = prices[idx]
    actual_price = prices[target_idx]
    actual_change = ((actual_price - prev_price) / prev_price) * 100
    actual_direction = actual_change > 0.1 ? 'UP' : (actual_change < -0.1 ? 'DOWN' : 'FLAT')

    predicted_magnitude = fpop_data[idx][:magnitude]
    difference = (predicted_magnitude - actual_change).abs
    correct = difference <= 1.0

    recent_fpop << {
      date: dates[target_idx],
      direction: fpop_data[idx][:direction],
      magnitude: fpop_data[idx][:magnitude],
      risk: fpop_data[idx][:risk],
      interpretation: fpop_data[idx][:interpretation],
      actual_change: actual_change.round(2),
      actual_direction: actual_direction,
      correct: correct,
      is_future: false
    }
  end

  # Future predictions
  fpop_data.last(fpop_period).each_with_index do |f, i|
    recent_fpop << {
      date: future_dates[i],
      direction: f[:direction],
      magnitude: f[:magnitude],
      risk: f[:risk],
      interpretation: f[:interpretation],
      actual_change: nil,
      actual_direction: nil,
      correct: nil,
      is_future: true
    }
  end

  recent_fpop
end

#calculate_all_indicators(opens, highs, lows, prices, volumes, n) ⇒ Object

Calculate all technical indicators



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# File 'lib/sqa_demo/sinatra/helpers/api_helpers.rb', line 23

def calculate_all_indicators(opens, highs, lows, prices, volumes, n)
  pad_array = ->(arr) { Array.new(n - arr.length, nil) + arr }

  # Price indicators
  rsi = pad_array.call(SQAI.rsi(prices, period: 14))
  macd_result = SQAI.macd(prices)
  macd_line = pad_array.call(macd_result[0])
  macd_signal = pad_array.call(macd_result[1])
  macd_hist = pad_array.call(macd_result[2])

  bb_result = SQAI.bbands(prices)
  bb_upper = pad_array.call(bb_result[0])
  bb_middle = pad_array.call(bb_result[1])
  bb_lower = pad_array.call(bb_result[2])

  # Moving averages
  sma_12 = pad_array.call(SQAI.sma(prices, period: 12))
  sma_20 = pad_array.call(SQAI.sma(prices, period: 20))
  sma_50 = pad_array.call(SQAI.sma(prices, period: 50))
  ema_20 = pad_array.call(SQAI.ema(prices, period: 20))
  wma_20 = pad_array.call(SQAI.wma(prices, period: 20))
  dema_20 = pad_array.call(SQAI.dema(prices, period: 20))
  tema_20 = pad_array.call(SQAI.tema(prices, period: 20))
  kama_30 = pad_array.call(SQAI.kama(prices, period: 30))

  # Momentum indicators
  stoch_result = SQAI.stoch(highs, lows, prices)
  stoch_slowk = pad_array.call(stoch_result[0])
  stoch_slowd = pad_array.call(stoch_result[1])
  mom_10 = pad_array.call(SQAI.mom(prices, period: 10))
  cci_14 = pad_array.call(SQAI.cci(highs, lows, prices, period: 14))
  willr_14 = pad_array.call(SQAI.willr(highs, lows, prices, period: 14))
  roc_10 = pad_array.call(SQAI.roc(prices, period: 10))
  adx_14 = pad_array.call(SQAI.adx(highs, lows, prices, period: 14))

  # Volatility indicators
  atr_14 = pad_array.call(SQAI.atr(highs, lows, prices, period: 14))

  # Volume indicators
  obv = pad_array.call(SQAI.obv(prices, volumes))
  ad = pad_array.call(SQAI.ad(highs, lows, prices, volumes))
  vol_sma_12 = pad_array.call(SQAI.sma(volumes, period: 12))
  vol_sma_20 = pad_array.call(SQAI.sma(volumes, period: 20))
  vol_sma_50 = pad_array.call(SQAI.sma(volumes, period: 50))
  vol_ema_12 = pad_array.call(SQAI.ema(volumes, period: 12))
  vol_ema_20 = pad_array.call(SQAI.ema(volumes, period: 20))

  {
    rsi: rsi, macd: macd_line, macd_signal: macd_signal, macd_hist: macd_hist,
    bb_upper: bb_upper, bb_middle: bb_middle, bb_lower: bb_lower,
    sma_12: sma_12, sma_20: sma_20, sma_50: sma_50, ema_20: ema_20,
    wma_20: wma_20, dema_20: dema_20, tema_20: tema_20, kama_30: kama_30,
    stoch_slowk: stoch_slowk, stoch_slowd: stoch_slowd, mom_10: mom_10,
    cci_14: cci_14, willr_14: willr_14, roc_10: roc_10, adx_14: adx_14,
    atr_14: atr_14, obv: obv, ad: ad,
    vol_sma_12: vol_sma_12, vol_sma_20: vol_sma_20, vol_sma_50: vol_sma_50,
    vol_ema_12: vol_ema_12, vol_ema_20: vol_ema_20
  }
end

#detect_candlestick_patterns(opens, highs, lows, prices, dates, n) ⇒ Object

Detect candlestick patterns



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# File 'lib/sqa_demo/sinatra/helpers/api_helpers.rb', line 84

def detect_candlestick_patterns(opens, highs, lows, prices, dates, n)
  pad_array = ->(arr) { Array.new(n - arr.length, nil) + arr }

  cdl_doji = pad_array.call(SQAI.cdl_doji(opens, highs, lows, prices))
  cdl_hammer = pad_array.call(SQAI.cdl_hammer(opens, highs, lows, prices))
  cdl_shootingstar = pad_array.call(SQAI.cdl_shootingstar(opens, highs, lows, prices))
  cdl_engulfing = pad_array.call(SQAI.cdl_engulfing(opens, highs, lows, prices))
  cdl_morningstar = pad_array.call(SQAI.cdl_morningstar(opens, highs, lows, prices))
  cdl_eveningstar = pad_array.call(SQAI.cdl_eveningstar(opens, highs, lows, prices))
  cdl_harami = pad_array.call(SQAI.cdl_harami(opens, highs, lows, prices))
  cdl_3whitesoldiers = pad_array.call(SQAI.cdl_3whitesoldiers(opens, highs, lows, prices))
  cdl_3blackcrows = pad_array.call(SQAI.cdl_3blackcrows(opens, highs, lows, prices))
  cdl_piercing = pad_array.call(SQAI.cdl_piercing(opens, highs, lows, prices))
  cdl_darkcloudcover = pad_array.call(SQAI.cdl_darkcloudcover(opens, highs, lows, prices))
  cdl_marubozu = pad_array.call(SQAI.cdl_marubozu(opens, highs, lows, prices))

  pattern_defs = {
    doji: { data: cdl_doji, name: 'Doji', type: :neutral },
    hammer: { data: cdl_hammer, name: 'Hammer', type: :fixed, signal: 'bullish' },
    shootingstar: { data: cdl_shootingstar, name: 'Shooting Star', type: :fixed, signal: 'bearish' },
    engulfing: { data: cdl_engulfing, name: 'Engulfing', type: :directional },
    morningstar: { data: cdl_morningstar, name: 'Morning Star', type: :fixed, signal: 'bullish' },
    eveningstar: { data: cdl_eveningstar, name: 'Evening Star', type: :fixed, signal: 'bearish' },
    harami: { data: cdl_harami, name: 'Harami', type: :directional },
    whitesoldiers: { data: cdl_3whitesoldiers, name: 'Three White Soldiers', type: :fixed, signal: 'bullish' },
    blackcrows: { data: cdl_3blackcrows, name: 'Three Black Crows', type: :fixed, signal: 'bearish' },
    piercing: { data: cdl_piercing, name: 'Piercing', type: :fixed, signal: 'bullish' },
    darkcloudcover: { data: cdl_darkcloudcover, name: 'Dark Cloud Cover', type: :fixed, signal: 'bearish' },
    marubozu: { data: cdl_marubozu, name: 'Marubozu', type: :directional }
  }

  detected_patterns = []
  pattern_defs.each do |_key, pdef|
    pdef[:data].each_with_index do |val, i|
      next if val.nil? || val == 0

      signal = case pdef[:type]
               when :neutral then 'neutral'
               when :fixed then pdef[:signal]
               when :directional then val > 0 ? 'bullish' : 'bearish'
               end

      detected_patterns << {
        date: dates[i],
        pattern: pdef[:name],
        signal: signal,
        strength: val.abs
      }
    end
  end

  detected_patterns.sort_by! { |p| p[:date] }.reverse!
  detected_patterns.first(20)
end

#filter_indicators_by_period(dates, indicators, period) ⇒ Object

Filter indicators by period



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# File 'lib/sqa_demo/sinatra/helpers/api_helpers.rb', line 140

def filter_indicators_by_period(dates, indicators, period)
  all_arrays = [dates] + indicators.values
  filtered = filter_by_period(*all_arrays, period: period)

  result = { dates: filtered[0] }
  indicators.keys.each_with_index do |key, i|
    result[key] = filtered[i + 1]
  end
  result
end

#resolve_strategy(strategy_name) ⇒ Object

Resolve strategy name to class



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# File 'lib/sqa_demo/sinatra/helpers/api_helpers.rb', line 10

def resolve_strategy(strategy_name)
  case strategy_name.upcase
  when 'RSI' then SQA::Strategy::RSI
  when 'SMA' then SQA::Strategy::SMA
  when 'EMA' then SQA::Strategy::EMA
  when 'MACD' then SQA::Strategy::MACD
  when 'BOLLINGERBANDS' then SQA::Strategy::BollingerBands
  when 'KBS' then SQA::Strategy::KBS
  else SQA::Strategy::RSI
  end
end