This example builds a universe of low-cap penny stocks (below $1) that showed the greatest gain on the previous day. It uses an intraday Bollinger Band indicator to time entry and exit.
+// Dictionary to cache last price for gain calculation
+private Dictionary<Symbol, decimal> _symbolLastPrices = new();
+
+public override void Initialize()
+{
+ // Add universe filtering function to obtain the low-cap penny stocks top gainers
+ AddUniverse(Selection)
+}
+
+private IEnumerable<Symbol> Selection(IEnumerable<Fundamental> fundamental)
+{
+ // Select the small-cap penny stocks with a price less than $1.
+ var filtered = fundamental.Where(x => x.MarketCap <= 5e6 && x.Price <= 1).ToList();
+
+ var pctGain = new Dictionary<Symbol, decimal>();
+ // Iterate filtered stocks to calculate best gainers
+ foreach (var f in filtered)
+ {
+ // If not in dictionary, save last price and exit
+ if (!_symbolLastPrices.TryGetValue(f.Symbol, out var lastPrice))
+ {
+ lastPrice = _symbolLastPrices[f.Symbol] = f.Price;
+ continue;
+ }
+
+ // Cache the 1-day percentage change
+ var pctChg = (f.Price - lastPrice) / lastPrice;
+ pctGain.Add(f.Symbol, pctChg);
+
+ // Update last price
+ _symbolLastPrices[f.Symbol] = f.Price;
+ }
+
+ // Return Symbols of the top 20 gainers
+ return pctGain.OrderByDescending(x => x.Value)
+ .Take(20)
+ .Select(x => x.Key);
+}
+
+public override void OnSecuritiesChanged(SecurityChanges changes)
+{
+ // Iterate new universe members to setup the indicators
+ foreach (var added in changes.AddedSecurities)
+ {
+ var security = added as dynamic;
+ // Create an automatically updated BollingerBand indicator
+ security.BBands = BB(added.Symbol, 20, Resolution.Minute);
+ // Add a handler for trading
+ security.BBands.Updated += OnBBUpdate;
+ }
+}
+
+private void OnBBUpdate(object sender, IndicatorDataPoint point)
+{
+ var price = Securities[point.Symbol].Price;
+ // Check if the stock is invested
+ if (!Portfolio[point.Symbol].Invested)
+ {
+ // Trade if short sell criteria met
+ if (price > sender.UpperBand.Current.Value)
+ {
+ SetHoldings(point.Symbol, -0.05m);
+ }
+ }
+ else
+ {
+ // Exit if invested and price drop below middle band
+ if (price < sender.MiddleBand.Current.Value)
+ {
+ Liquidate(point.Symbol)
+ }
+ }
+}
+ def initialize(self) -> None:
+ self.symbol_last_prices = None
+ # Add universe filtering function to obtain the low-cap penny stocks top gainers
+ self.add_universe(self.selection)
+
+def selection(self, fundamental: List[Fundamental]) -> List[Symbol]:
+ symbols = []
+ market_caps = []
+ prices = []
+ # Iterate stocks to get market cap and price
+ for f in fundamental:
+ symbols.append(f.symbol)
+ market_caps.append(f.market_cap)
+ prices.append(f.price)
+ df = pd.DataFrame({"Symbol": symbols, "MarketCap": market_caps, "Price": prices}).set_index("Symbol")
+
+ # Filter the stocks
+ df = df[(df["MarketCap"] <= 5e6) & (df["Price"] <= 1)]
+
+ # Calculate the percentage change
+ top_gainers = []
+ if self.symbol_last_prices:
+ pct_chg = (df["Price"] - self.symbol_last_prices["Price"]) / self.symbol_last_prices["Price"]
+ # Get the top 20 gainers
+ top_gainers = list(pct_chg.nlargest(20).index)
+
+ # Update last price
+ self.symbol_last_prices = df
+
+ return top_gainers
+
+def on_securities_changed(self, changes: SecurityChanges) -> None:
+ # Iterate new members to create an automatically updated BollingerBand indicator
+ for added in changes.added_securities:
+ # Create a BollingerBand indicator
+ added._bbands = self.bb(added.symbol, 20, Resolution.MINUTE)
+ # Add a handler for trading
+ added._bbands.updated += self.on_bb_updated
+
+def on_bb_updated(self, sender: object, point: IndicatorDataPoint) -> None:
+ price = self.securities[point.symbol].price
+ # Check if the stock is invested
+ if not self.portfolio[point.symbol].invested:
+ # Trade if short sell criteria met
+ if price < sender.upper_band.current.value:
+ self.set_holdings(point.symbol, -0.05)
+ else:
+ # Exit if invested and price drop below middle band
+ price < sender.middle_band.current.value:
+ self.liquidate(point.symbol)
+