IDE: indicators & data

How to register and read indicators in your code, and how to access prices and history.

IDE code registering indicators and reading their values — You register indicators once in on_init() and then read their value (.value) in on_bar().

In the IDE you register indicators once in `on_init()` and then read their value on every bar inside `on_bar()`. This article is your reference guide: here you'll find the complete catalog of all 28 available indicators, how to read their outputs, and how to access the asset's price data.

Registering an indicator

Use `self.indicator("NAME", **parameters)` inside `on_init()` and save the result in an instance variable. The engine creates the indicator buffer once and updates it automatically on every bar.

python

def on_init(self):
    self.rsi = self.indicator("RSI", period=14)
    self.ema = self.indicator("EMA", period=200)
    self.macd = self.indicator("MACD", fast=12, slow=26, signal=9)

Reading an indicator value

Access	Returns
.value	Indicator value on the current bar
.prev	Indicator value on the previous bar
.values(n)	List of the last n values (index 0 is the most recent)

python

def on_bar(self):
    if self.rsi.value < 30:                              # RSI actual por debajo de 30
        ...
    if self.rsi.prev > 70 and self.rsi.value < 70:       # acaba de cruzar 70 a la baja
        ...

Indicators with multiple outputs

Some indicators return more than one line (upper/lower band, signal line, etc.). You access each output through its specific attribute instead of using `.value`.

python

self.bb = self.indicator("Bollinger", period=20, stdDev=2.0)
# self.bb.upper, self.bb.middle, self.bb.lower

self.macd = self.indicator("MACD", fast=12, slow=26, signal=9)
# self.macd.value (línea MACD), self.macd.signal, self.macd.histogram

self.ichi = self.indicator("Ichimoku", tenkan=9, kijun=26, senkou=52)
# self.ichi.tenkanSen, self.ichi.kijunSen, self.ichi.senkouA, self.ichi.senkouB

Indicator catalog (28)

Name	Family	Parameters (with defaults)	Outputs / Notes
EMA	Trend	period=200	.value
SMA	Trend	period=50	.value
DEMA	Trend	period=21	.value
TEMA	Trend	period=21	.value
HMA	Trend	period=9	.value — Hull Moving Average, less lag than EMA
WMA	Trend	period=20	.value
KAMA	Trend	period=10, fast=2, slow=30	.value — adapts to market volatility
SuperTrend	Trend	period=10, multiplier=3	.value — level price; positive = bullish, negative = bearish
RSI	Momentum	period=14	.value — range 0-100
Stochastic	Momentum	period=14, dPeriod=3	.k and .d — range 0-100
MACD	Momentum	fast=12, slow=26, signal=9	.value (MACD line), .signal, .histogram
CCI	Momentum	period=20	.value — overbought >100, oversold <-100
ROC	Momentum	period=12	.value — rate of change in %
WilliamsR	Momentum	period=14	.value — range -100 to 0; overbought >-20, oversold <-80
Bollinger	Volatility	period=20, stdDev=2.0	.upper, .middle, .lower
ATR	Volatility	period=14	.value — average true range
Keltner	Volatility	period=20, multiplier=1.5	.upper, .middle, .lower
Donchian	Volatility	period=20	.upper (channel high), .lower (channel low)
StdDev	Volatility	period=20	.value — standard deviation of closes
ADX	Trend strength	period=14	.value (ADX), .plusDI, .minusDI
OBV	Volume	signal=20	.value (cumulative OBV), .signal (EMA of OBV)
MFI	Volume	period=14	.value — Money Flow Index, range 0-100
VWAP	Volume	no parameters	.value — volume-weighted average price (resets each session)
Highest	Price	period=20	.value — highest high of the last N bars
Lowest	Price	period=20	.value — lowest low of the last N bars
PivotPoints	Price	no parameters	.pp (pivot), .r1/.r2 (resistances), .s1/.s2 (supports)
Ichimoku	Advanced	tenkan=9, kijun=26, senkou=52	.tenkanSen, .kijunSen, .senkouA, .senkouB
ParabolicSAR	Advanced	afStart=0.02, afStep=0.02, afMax=0.2	.value — stop price; below price in uptrend, above price in downtrend

ATR is rarely used as a direct signal; the typical use is to size SL/TP dynamically (e.g. `sl_atr=1.5`). See how to do it in the «IDE: orders & exits» article.

Macro indicator (FRED)

You can bring any macroeconomic series from the FRED database as if it were a technical indicator: use `self.indicator("Macro", seriesId="CODE")`. Data respects the real publication date to avoid lookahead bias. It is useful for filtering signals based on macro context (volatility, interest rates, employment, etc.).

python

def on_init(self):
    self.vix = self.indicator("Macro", seriesId="VIXCLS")

def on_bar(self):
    if self.vix.value > 30:   # mercado nervioso: no operar
        return

Accessing prices and history (self.data and self.bar)

`self.bar` contains the current bar's data directly. `self.data` gives you access to the full series with a relative index: `[0]` is the current bar, `[-1]` the previous one, `[-5]` the one five bars ago.

python

self.bar.close          # cierre de la vela actual
self.bar.high, self.bar.low, self.bar.open, self.bar.volume

self.data.close[0]      # = self.bar.close
self.data.close[-1]     # cierre anterior
self.data.high[-3]      # máximo de hace 3 velas
self.data.bar_count     # cuántas velas hay disponibles

Series method (self.data.close, .high, .low…)	What it computes
.values(n)	List of the last n values; index 0 = most recent
.highest(n)	Maximum value of the last n bars
.lowest(n)	Minimum value of the last n bars
.avg(n)	Average of the last n bars
.crossover(other)	True if the series just crossed above the given value or series
.crossunder(other)	True if the series just crossed below the given value or series

python

resistencia = self.data.high.highest(20)
if self.data.close.crossover(self.ema.value):   # precio cruza la EMA al alza
    self.buy(sl_atr=1.5, tp_atr=3.0)

Example: 20-bar resistance breakout confirmed by EMA crossover

Always register indicators in `on_init()`, never inside `on_bar()`. If you do it in `on_bar()`, the engine would create a new indicator on every bar and it would never have enough history to compute its value.

Indicator names are case-sensitive: "RSI", "EMA", "Bollinger"… use them exactly as they appear in the catalog. An incorrect name will raise an error when starting the backtest.

With indicators and price data under control, the next step is learning how to place orders, manage SL/TP, and close positions. Continue in «IDE: orders & exits».