src/Indicators.jl

"""
Indicators.jl — Vectorized technical indicator library.
Standalone module. No includes. No external deps beyond Statistics.
"""
module Indicators

using Statistics

export sma, ema, wma, tema, dema,
       rsi, macd, stoch, cci, williams_r,
       atr, bbands, keltner, donchian, adx,
       vwap, obv, cmf, zscore, std_dev,
       momentum, roc, highest, lowest,
       crossover, crossunder

# ── Trend ─────────────────────────────────────────────

function sma(x::Vector{Float64}, n::Int)::Vector{Float64}
    len = length(x); out = fill(NaN, len); s = 0.0
    for i in 1:len
        s += x[i]
        if i >= n
            i > n && (s -= x[i-n])
            out[i] = s / n
        end
    end
    return out
end

function ema(x::Vector{Float64}, n::Int)::Vector{Float64}
    len = length(x); out = fill(NaN, len)
    k = 2.0 / (n + 1.0)
    # seed: SMA of first n non-NaN values
    s = 0.0; cnt = 0; seed_i = 0
    for i in 1:len
        isnan(x[i]) && continue
        s += x[i]; cnt += 1
        if cnt == n
            seed_i = i; out[i] = s / n
            val = out[i]
            for j in (i+1):len
                isnan(x[j]) && continue
                val = x[j] * k + val * (1.0 - k)
                out[j] = val
            end
            break
        end
    end
    return out
end

function wma(x::Vector{Float64}, n::Int)::Vector{Float64}
    len = length(x); out = fill(NaN, len)
    ws = n * (n+1) / 2.0
    for i in n:len
        s = 0.0
        for j in 1:n; s += x[i-n+j] * j; end
        out[i] = s / ws
    end
    return out
end

tema(x::Vector{Float64}, n::Int) = let e1=ema(x,n),e2=ema(e1,n),e3=ema(e2,n); 3.0.*e1 .- 3.0.*e2 .+ e3 end
dema(x::Vector{Float64}, n::Int) = let e1=ema(x,n),e2=ema(e1,n); 2.0.*e1 .- e2 end

# ── Oscillators ───────────────────────────────────────

function rsi(close::Vector{Float64}, n::Int=14)::Vector{Float64}
    len = length(close); out = fill(NaN, len)
    ag = 0.0; al = 0.0
    for i in 2:(n+1)
        i > len && break
        d = close[i] - close[i-1]
        d > 0 ? (ag += d) : (al += abs(d))
    end
    ag /= n; al /= n
    n+1 <= len && (out[n+1] = 100.0 - 100.0/(1.0 + (al==0 ? 1e10 : ag/al)))
    for i in (n+2):len
        d = close[i] - close[i-1]
        g = d > 0 ? d : 0.0; l = d < 0 ? abs(d) : 0.0
        ag = (ag*(n-1)+g)/n; al = (al*(n-1)+l)/n
        out[i] = 100.0 - 100.0/(1.0 + (al==0 ? 1e10 : ag/al))
    end
    return out
end

function macd(close::Vector{Float64}; fast::Int=12, slow::Int=26, sig::Int=9)
    ml = ema(close,fast) .- ema(close,slow)
    sl = ema(ml, sig)
    return ml, sl, ml .- sl
end

function stoch(high::Vector{Float64}, low::Vector{Float64}, close::Vector{Float64};
               k::Int=14, d::Int=3)
    len = length(close); K = fill(NaN, len)
    for i in k:len
        hh = maximum(high[i-k+1:i]); ll = minimum(low[i-k+1:i])
        K[i] = hh==ll ? 50.0 : 100.0*(close[i]-ll)/(hh-ll)
    end
    return K, sma(K, d)
end

function cci(high::Vector{Float64}, low::Vector{Float64}, close::Vector{Float64}, n::Int=20)::Vector{Float64}
    len = length(close); tp = (high.+low.+close)./3.0; out = fill(NaN, len)
    for i in n:len
        w = tp[i-n+1:i]; m = mean(w); md = mean(abs.(w.-m))
        out[i] = md==0 ? 0.0 : (tp[i]-m)/(0.015*md)
    end
    return out
end

function williams_r(high::Vector{Float64}, low::Vector{Float64}, close::Vector{Float64}, n::Int=14)::Vector{Float64}
    len = length(close); out = fill(NaN, len)
    for i in n:len
        hh = maximum(high[i-n+1:i]); ll = minimum(low[i-n+1:i])
        out[i] = hh==ll ? -50.0 : -100.0*(hh-close[i])/(hh-ll)
    end
    return out
end

momentum(x::Vector{Float64}, n::Int=10) = let len=length(x),out=fill(NaN,len); for i in (n+1):len; out[i]=x[i]-x[i-n]; end; out end
roc(x::Vector{Float64}, n::Int=10) = let len=length(x),out=fill(NaN,len); for i in (n+1):len; out[i]=x[i-n]==0 ? 0.0 : 100.0*(x[i]-x[i-n])/x[i-n]; end; out end

# ── Volatility ────────────────────────────────────────

function _tr(high::Vector{Float64}, low::Vector{Float64}, close::Vector{Float64})::Vector{Float64}
    len = length(close); tr = fill(NaN, len)
    tr[1] = high[1]-low[1]
    for i in 2:len; tr[i] = max(high[i]-low[i], abs(high[i]-close[i-1]), abs(low[i]-close[i-1])); end
    return tr
end

atr(high::Vector{Float64}, low::Vector{Float64}, close::Vector{Float64}, n::Int=14) = ema(_tr(high,low,close), n)

function bbands(close::Vector{Float64}, n::Int=20, k::Float64=2.0)
    len = length(close); mid = sma(close,n); sd = fill(NaN, len)
    for i in n:len; sd[i] = std(close[i-n+1:i]; corrected=false); end
    return mid.+k.*sd, mid, mid.-k.*sd
end

function keltner(high::Vector{Float64}, low::Vector{Float64}, close::Vector{Float64}, n::Int=20, k::Float64=2.0)
    mid = ema(close,n); a = atr(high,low,close,n)
    return mid.+k.*a, mid, mid.-k.*a
end

function donchian(high::Vector{Float64}, low::Vector{Float64}, n::Int=20)
    len = length(high); u = fill(NaN,len); l = fill(NaN,len)
    for i in n:len; u[i]=maximum(high[i-n+1:i]); l[i]=minimum(low[i-n+1:i]); end
    return u, (u.+l)./2.0, l
end

function std_dev(x::Vector{Float64}, n::Int=20)::Vector{Float64}
    len = length(x); out = fill(NaN, len)
    for i in n:len; out[i] = std(x[i-n+1:i]; corrected=false); end
    return out
end

function zscore(x::Vector{Float64}, n::Int=20)::Vector{Float64}
    mu = sma(x,n); sd = std_dev(x,n); out = fill(NaN, length(x))
    for i in eachindex(x)
        !isnan(mu[i]) && !isnan(sd[i]) && sd[i]>0 && (out[i]=(x[i]-mu[i])/sd[i])
    end
    return out
end

# ── Trend strength ────────────────────────────────────

function adx(high::Vector{Float64}, low::Vector{Float64}, close::Vector{Float64}, n::Int=14)
    tr = _tr(high,low,close)
    up = diff(vcat(high[1],high)); dn = diff(vcat(low[1],low))
    pdm = map((u,d)->u>d&&u>0 ? u : 0.0, up, dn)
    ndm = map((u,d)->d>u&&d>0 ? d : 0.0, up, dn)
    sm=ema(tr,n); pdi=100.0.*ema(pdm,n)./(sm.+1e-10); ndi=100.0.*ema(ndm,n)./(sm.+1e-10)
    dx = 100.0.*abs.(pdi.-ndi)./(pdi.+ndi.+1e-10)
    return ema(dx,n), pdi, ndi
end

# ── Volume ────────────────────────────────────────────

function vwap(high::Vector{Float64}, low::Vector{Float64}, close::Vector{Float64}, volume::Vector{Float64})::Vector{Float64}
    tp = (high.+low.+close)./3.0
    return cumsum(tp.*volume)./(cumsum(volume).+1e-10)
end

function obv(close::Vector{Float64}, volume::Vector{Float64})::Vector{Float64}
    len = length(close); out = zeros(Float64, len); out[1] = volume[1]
    for i in 2:len
        d = close[i]-close[i-1]
        out[i] = out[i-1] + (d>0 ? volume[i] : d<0 ? -volume[i] : 0.0)
    end
    return out
end

function cmf(high::Vector{Float64}, low::Vector{Float64}, close::Vector{Float64},
             volume::Vector{Float64}, n::Int=20)::Vector{Float64}
    len = length(close); out = fill(NaN, len)
    hl = high.-low
    mfv = map((c,l,h,hl)->hl==0 ? 0.0 : (2c-l-h)/hl, close,low,high,hl).*volume
    for i in n:len
        sv = sum(volume[i-n+1:i])
        out[i] = sv==0 ? 0.0 : sum(mfv[i-n+1:i])/sv
    end
    return out
end

# ── Utilities ─────────────────────────────────────────

highest(x::Vector{Float64}, n::Int) = let len=length(x),out=fill(NaN,len); for i in n:len; out[i]=maximum(x[i-n+1:i]); end; out end
lowest(x::Vector{Float64},  n::Int) = let len=length(x),out=fill(NaN,len); for i in n:len; out[i]=minimum(x[i-n+1:i]); end; out end

function crossover(a::Vector{Float64}, b::Vector{Float64})::Vector{Bool}
    len=length(a); out=fill(false,len)
    for i in 2:len; out[i] = a[i]>b[i] && a[i-1]<=b[i-1]; end
    return out
end

function crossunder(a::Vector{Float64}, b::Vector{Float64})::Vector{Bool}
    len=length(a); out=fill(false,len)
    for i in 2:len; out[i] = a[i]<b[i] && a[i-1]>=b[i-1]; end
    return out
end

end # module Indicators