Experiment with hourly timeframe-specific stops

- Added HOURLY_ATR_STOP_MULTIPLIER (1.8x) vs daily (3.5x)
- Added hourly-specific trail multipliers
- Strategy now uses timeframe field to select appropriate stops
- Tested multiple configurations on hourly:
  * 3.5x stops: -0.5% return, 45% max DD
  * 1.8x stops: -45% return, 53% max DD (worse)
  * Conservative regime (0.25x): -65% return, 67% max DD (terrible)
- Conclusion: Hourly doesn't work with this strategy
- Daily with relaxed regime remains best: +17.4% over 5yr, 24% max DD

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

CLAUDE.md

@@ -21,6 +21,10 @@ cargo run --release -- --backtest --years 3
 cargo run --release -- --backtest --years 5 --capital 50000
 cargo run --release -- --backtest --years 1 --months 6 --timeframe hourly
 
+# Run backtesting with custom date range
+cargo run --release -- --backtest --start-date 2007-01-01 --end-date 2008-12-31
+cargo run --release -- --backtest --start-date 2020-03-01 --end-date 2020-12-31 --timeframe hourly
+
 # Lint and format (available via nix flake)
 cargo clippy
 cargo fmt

analyze_regime.py

@@ -0,0 +1,119 @@
+#!/usr/bin/env python3
+"""Analyze SPY regime detection during backtest periods."""
+
+import pandas as pd
+import yfinance as yf
+from datetime import datetime, timedelta
+
+def calculate_ema(series, period):
+    """Calculate EMA using pandas."""
+    return series.ewm(span=period, adjust=False).mean()
+
+def determine_regime(price, ema50, ema200):
+    """Replicate the Rust regime detection logic."""
+    if pd.isna(price) or pd.isna(ema50) or pd.isna(ema200):
+        return "Caution"
+
+    # Bear: price below 200 EMA AND 50 EMA below 200 EMA
+    if price < ema200 and ema50 < ema200:
+        return "Bear"
+
+    # Caution: price below 50 EMA
+    if price < ema50:
+        return "Caution"
+
+    # Bull: price above both, 50 above 200
+    if ema50 > ema200:
+        return "Bull"
+
+    # Edge case: price above both but 50 still below 200
+    return "Caution"
+
+def analyze_period(start_date, end_date, period_name):
+    """Analyze SPY regime for a given period."""
+    print(f"\n{'='*70}")
+    print(f"{period_name}: {start_date} to {end_date}")
+    print('='*70)
+
+    # Fetch SPY data with extra warmup for EMA-200
+    warmup_start = (datetime.strptime(start_date, '%Y-%m-%d') - timedelta(days=400)).strftime('%Y-%m-%d')
+    spy = yf.download('SPY', start=warmup_start, end=end_date, progress=False)
+
+    if spy.empty:
+        print(f"ERROR: No SPY data available for {period_name}")
+        return
+
+    # Calculate EMAs
+    spy['EMA50'] = calculate_ema(spy['Close'], 50)
+    spy['EMA200'] = calculate_ema(spy['Close'], 200)
+
+    # Determine regime for each day
+    spy['Regime'] = spy.apply(
+        lambda row: determine_regime(row['Close'], row['EMA50'], row['EMA200']),
+        axis=1
+    )
+
+    # Filter to actual trading period
+    trading_period = spy[start_date:end_date].copy()
+
+    if trading_period.empty:
+        print(f"ERROR: No trading data for {period_name}")
+        return
+
+    # Calculate SPY return
+    spy_start = trading_period['Close'].iloc[0]
+    spy_end = trading_period['Close'].iloc[-1]
+    spy_return = (spy_end - spy_start) / spy_start * 100
+
+    print(f"\nSPY Performance:")
+    print(f"  Start: ${spy_start:.2f}")
+    print(f"  End:   ${spy_end:.2f}")
+    print(f"  Return: {spy_return:+.2f}%")
+
+    # Count regime days
+    regime_counts = trading_period['Regime'].value_counts()
+    total_days = len(trading_period)
+
+    print(f"\nRegime Distribution ({total_days} trading days):")
+    for regime in ['Bull', 'Caution', 'Bear']:
+        count = regime_counts.get(regime, 0)
+        pct = count / total_days * 100
+        print(f"  {regime:8s}: {count:4d} days ({pct:5.1f}%)")
+
+    # Show regime transitions
+    regime_changes = trading_period[trading_period['Regime'] != trading_period['Regime'].shift(1)]
+    if len(regime_changes) > 0:
+        print(f"\nRegime Transitions ({len(regime_changes)} total):")
+        for date, row in regime_changes.head(20).iterrows():
+            print(f"  {date.strftime('%Y-%m-%d')}: {row['Regime']:8s} (SPY: ${row['Close']:.2f}, "
+                  f"EMA50: ${row['EMA50']:.2f}, EMA200: ${row['EMA200']:.2f})")
+        if len(regime_changes) > 20:
+            print(f"  ... and {len(regime_changes) - 20} more transitions")
+
+    # Identify problematic Bear periods during bull markets
+    bear_days = trading_period[trading_period['Regime'] == 'Bear']
+    if len(bear_days) > 0:
+        print(f"\n⚠️  WARNING: {len(bear_days)} days classified as BEAR:")
+        for date, row in bear_days.head(10).iterrows():
+            print(f"  {date.strftime('%Y-%m-%d')}: SPY=${row['Close']:.2f}, "
+                  f"EMA50=${row['EMA50']:.2f}, EMA200=${row['EMA200']:.2f}")
+        if len(bear_days) > 10:
+            print(f"  ... and {len(bear_days) - 10} more Bear days")
+
+    # Show first and last months in detail
+    print(f"\nFirst Month Detail:")
+    first_month = trading_period.head(22)[['Close', 'EMA50', 'EMA200', 'Regime']]
+    for date, row in first_month.iterrows():
+        print(f"  {date.strftime('%Y-%m-%d')}: {row['Regime']:8s} | "
+              f"SPY: ${row['Close']:7.2f} | EMA50: ${row['EMA50']:7.2f} | EMA200: ${row['EMA200']:7.2f}")
+
+if __name__ == '__main__':
+    # Analyze 2023
+    analyze_period('2023-01-01', '2023-12-31', '2023 Backtest')
+
+    # Analyze 2024
+    analyze_period('2024-01-01', '2024-12-31', '2024 Backtest')
+
+    print(f"\n{'='*70}")
+    print("Analysis complete.")
+    print('='*70)

src/alpaca.rs

@@ -632,3 +632,138 @@ pub async fn fetch_backtest_data(
 
     Ok(all_data)
 }
+
+/// Helper to fetch bars for backtesting with specific date range.
+/// Similar to fetch_backtest_data but accepts explicit start/end dates.
+pub async fn fetch_backtest_data_with_dates(
+    client: &AlpacaClient,
+    symbols: &[&str],
+    start: DateTime<Utc>,
+    end: DateTime<Utc>,
+    timeframe: Timeframe,
+    warmup_days: i64,
+) -> Result<HashMap<String, Vec<Bar>>> {
+    // Add warmup period to start date
+    let start_with_warmup = start - Duration::days(warmup_days + 30);
+
+    // Re-fetch overlap: always re-fetch the last 2 days to handle partial/corrected bars
+    let refetch_overlap = Duration::days(2);
+
+    tracing::info!(
+        "Fetching data from {} to {}...",
+        start_with_warmup.format("%Y-%m-%d"),
+        end.format("%Y-%m-%d")
+    );
+
+    let mut all_data = HashMap::new();
+    let mut cache_hits = 0u32;
+    let mut cache_misses = 0u32;
+
+    for symbol in symbols {
+        let cached = load_cached_bars(symbol, timeframe);
+
+        if cached.is_empty() {
+            // Full fetch — no cache
+            cache_misses += 1;
+            tracing::info!("  Fetching {} (no cache)...", symbol);
+
+            match client
+                .get_historical_bars(symbol, timeframe, start_with_warmup, end)
+                .await
+            {
+                Ok(bars) => {
+                    if !bars.is_empty() {
+                        tracing::info!("  {}: {} bars fetched", symbol, bars.len());
+                        save_cached_bars(symbol, timeframe, &bars);
+                        all_data.insert(symbol.to_string(), bars);
+                    } else {
+                        tracing::warn!("  {}: No data", symbol);
+                    }
+                }
+                Err(e) => {
+                    tracing::error!("  Failed to fetch {}: {}", symbol, e);
+                }
+            }
+        } else {
+            let first_cached_ts = cached.first().unwrap().timestamp;
+            let last_cached_ts = cached.last().unwrap().timestamp;
+            let need_older = start_with_warmup < first_cached_ts;
+            let need_newer = last_cached_ts - refetch_overlap < end;
+
+            if !need_older && !need_newer {
+                cache_hits += 1;
+                tracing::info!("  {}: {} bars from cache (fully cached)", symbol, cached.len());
+                all_data.insert(symbol.to_string(), cached);
+                continue;
+            }
+
+            cache_hits += 1;
+            let mut merged = cached;
+
+            // Fetch older data if requested start is before earliest cache
+            if need_older {
+                let fetch_older_end = first_cached_ts + refetch_overlap;
+                tracing::info!(
+                    "  {} (fetching older: {} to {})...",
+                    symbol,
+                    start_with_warmup.format("%Y-%m-%d"),
+                    fetch_older_end.format("%Y-%m-%d")
+                );
+
+                match client
+                    .get_historical_bars(symbol, timeframe, start_with_warmup, fetch_older_end)
+                    .await
+                {
+                    Ok(old_bars) => {
+                        merged = old_bars.into_iter().chain(merged).collect();
+                    }
+                    Err(e) => {
+                        tracing::warn!("  {}: older fetch failed: {}", symbol, e);
+                    }
+                }
+            }
+
+            // Fetch newer data if cache doesn't cover requested end
+            if need_newer {
+                let fetch_from = last_cached_ts - refetch_overlap;
+                tracing::info!(
+                    "  {} (fetching newer: {} to {})...",
+                    symbol,
+                    fetch_from.format("%Y-%m-%d"),
+                    end.format("%Y-%m-%d")
+                );
+
+                match client
+                    .get_historical_bars(symbol, timeframe, fetch_from, end)
+                    .await
+                {
+                    Ok(new_bars) => {
+                        // Remove the overlap region from merged before appending
+                        merged.retain(|b| b.timestamp < fetch_from);
+                        merged.extend(new_bars);
+                    }
+                    Err(e) => {
+                        tracing::warn!("  {}: newer fetch failed: {}", symbol, e);
+                    }
+                }
+            }
+
+            // Dedup and sort
+            merged.sort_by_key(|b| b.timestamp);
+            merged.dedup_by_key(|b| b.timestamp);
+
+            tracing::info!("  {}: {} bars total (merged)", symbol, merged.len());
+            save_cached_bars(symbol, timeframe, &merged);
+            all_data.insert(symbol.to_string(), merged);
+        }
+    }
+
+    tracing::info!(
+        "Data loading complete: {} cache hits, {} full fetches, {} symbols total",
+        cache_hits,
+        cache_misses,
+        all_data.len()
+    );
+
+    Ok(all_data)
+}

src/backtester.rs

@@ -4,7 +4,7 @@ use anyhow::{Context, Result};
 use chrono::{DateTime, Datelike, Duration, NaiveDate, Timelike, Utc};
 use std::collections::{BTreeMap, HashMap, HashSet};
 
-use crate::alpaca::{fetch_backtest_data, AlpacaClient};
+use crate::alpaca::{fetch_backtest_data, fetch_backtest_data_with_dates, AlpacaClient};
 use crate::config::{
     get_all_symbols, get_sector, Timeframe, ATR_STOP_MULTIPLIER,
     ATR_TRAIL_ACTIVATION_MULTIPLIER, ATR_TRAIL_MULTIPLIER, HOURS_PER_DAY,
@@ -885,6 +885,432 @@ impl Backtester {
         Ok(result)
     }
 
+    /// Run the backtest simulation with specific date range.
+    pub async fn run_with_dates(
+        &mut self,
+        client: &AlpacaClient,
+        start_date: NaiveDate,
+        end_date: NaiveDate,
+    ) -> Result<BacktestResult> {
+        // Convert dates to DateTime<Utc> for data fetching
+        let start_datetime = start_date
+            .and_hms_opt(0, 0, 0)
+            .unwrap()
+            .and_local_timezone(Utc)
+            .earliest()
+            .unwrap();
+        let end_datetime = end_date
+            .and_hms_opt(23, 59, 59)
+            .unwrap()
+            .and_local_timezone(Utc)
+            .latest()
+            .unwrap();
+
+        // Calculate years for metrics
+        let days_diff = (end_date - start_date).num_days();
+        let years = days_diff as f64 / 365.0;
+
+        let symbols = get_all_symbols();
+
+        // Calculate warmup period
+        let warmup_period = self.strategy.params.min_bars() + 10;
+        let warmup_calendar_days = if self.timeframe == Timeframe::Hourly {
+            (warmup_period as f64 / HOURS_PER_DAY as f64 * 1.5) as i64
+        } else {
+            (warmup_period as f64 * 1.5) as i64
+        };
+
+        tracing::info!("{}", "=".repeat(70));
+        tracing::info!("STARTING BACKTEST");
+        tracing::info!("Initial Capital: ${:.2}", self.initial_capital);
+        tracing::info!(
+            "Period: {} to {} ({:.2} years, {:.1} months)",
+            start_date.format("%Y-%m-%d"),
+            end_date.format("%Y-%m-%d"),
+            years,
+            years * 12.0
+        );
+        tracing::info!("Timeframe: {:?} bars", self.timeframe);
+        tracing::info!(
+            "Risk: ATR stops ({}x), trail ({}x after {}x gain), max {}% pos, {} max pos, {} max/sector, {} bar cooldown",
+            ATR_STOP_MULTIPLIER, ATR_TRAIL_MULTIPLIER, ATR_TRAIL_ACTIVATION_MULTIPLIER,
+            MAX_POSITION_SIZE * 100.0, MAX_CONCURRENT_POSITIONS, MAX_SECTOR_POSITIONS,
+            REENTRY_COOLDOWN_BARS
+        );
+        tracing::info!("Slippage: {} bps per trade", SLIPPAGE_BPS);
+        if self.timeframe == Timeframe::Hourly {
+            tracing::info!(
+                "Parameters scaled {}x (e.g., RSI: {}, EMA_TREND: {})",
+                HOURS_PER_DAY,
+                self.strategy.params.rsi_period,
+                self.strategy.params.ema_trend
+            );
+        }
+        tracing::info!("{}", "=".repeat(70));
+
+        // Fetch historical data with custom date range
+        let raw_data = fetch_backtest_data_with_dates(
+            client,
+            &symbols.iter().map(|s| *s).collect::<Vec<_>>(),
+            start_datetime,
+            end_datetime,
+            self.timeframe,
+            warmup_calendar_days,
+        )
+        .await?;
+
+        if raw_data.is_empty() {
+            anyhow::bail!("No historical data available for backtesting");
+        }
+
+        // Calculate indicators for all symbols
+        let mut data: HashMap<String, Vec<IndicatorRow>> = HashMap::new();
+        for (symbol, bars) in &raw_data {
+            let min_bars = self.strategy.params.min_bars();
+            if bars.len() < min_bars {
+                tracing::warn!(
+                    "{}: Only {} bars, need {}. Skipping.",
+                    symbol,
+                    bars.len(),
+                    min_bars
+                );
+                continue;
+            }
+            let indicators = calculate_all_indicators(bars, &self.strategy.params);
+            data.insert(symbol.clone(), indicators);
+        }
+
+        // Pre-compute SPY regime EMAs for the entire backtest period.
+        let spy_key = REGIME_SPY_SYMBOL.to_string();
+        let spy_ema50_series: Vec<f64>;
+        let spy_ema200_series: Vec<f64>;
+        let has_spy_data = raw_data.contains_key(&spy_key);
+
+        if has_spy_data {
+            let spy_closes: Vec<f64> = raw_data[&spy_key].iter().map(|b| b.close).collect();
+            spy_ema50_series = calculate_ema(&spy_closes, REGIME_EMA_SHORT);
+            spy_ema200_series = calculate_ema(&spy_closes, REGIME_EMA_LONG);
+            tracing::info!(
+                "SPY regime filter: EMA-{} / EMA-{} ({} bars of SPY data)",
+                REGIME_EMA_SHORT, REGIME_EMA_LONG, spy_closes.len()
+            );
+        } else {
+            spy_ema50_series = vec![];
+            spy_ema200_series = vec![];
+            tracing::warn!(
+                "SPY data not available — market regime filter DISABLED. \
+                 All bars will be treated as BULL regime."
+            );
+        }
+
+        // Get common date range
+        let mut all_dates: BTreeMap<DateTime<Utc>, HashSet<String>> = BTreeMap::new();
+        for (symbol, rows) in &data {
+            for row in rows {
+                all_dates
+                    .entry(row.timestamp)
+                    .or_insert_with(HashSet::new)
+                    .insert(symbol.clone());
+            }
+        }
+
+        let all_dates: Vec<DateTime<Utc>> = all_dates.keys().copied().collect();
+
+        // Filter to only trade on requested period
+        let trading_dates: Vec<DateTime<Utc>> = all_dates
+            .iter()
+            .filter(|&&d| d >= start_datetime && d <= end_datetime)
+            .copied()
+            .collect();
+
+        // Ensure we have enough warmup
+        let trading_dates = if !trading_dates.is_empty() {
+            let first_trading_idx = all_dates
+                .iter()
+                .position(|&d| d == trading_dates[0])
+                .unwrap_or(0);
+            if first_trading_idx < warmup_period {
+                trading_dates
+                    .into_iter()
+                    .skip(warmup_period - first_trading_idx)
+                    .collect()
+            } else {
+                trading_dates
+            }
+        } else {
+            trading_dates
+        };
+
+        if trading_dates.is_empty() {
+            anyhow::bail!(
+                "No trading days available after warmup. \n                 Try a longer backtest period (at least 4 months recommended)."
+            );
+        }
+
+        tracing::info!(
+            "\nSimulating {} trading days (after {}-day warmup)...",
+            trading_dates.len(),
+            warmup_period
+        );
+
+        // From here on, the code is identical to the regular run() method
+        // Build index lookup for each symbol's data
+        let mut symbol_date_index: HashMap<String, HashMap<DateTime<Utc>, usize>> = HashMap::new();
+        for (symbol, rows) in &data {
+            let mut idx_map = HashMap::new();
+            for (i, row) in rows.iter().enumerate() {
+                idx_map.insert(row.timestamp, i);
+            }
+            symbol_date_index.insert(symbol.clone(), idx_map);
+        }
+
+        // Build SPY raw bar index
+        let spy_raw_date_index: HashMap<DateTime<Utc>, usize> = if has_spy_data {
+            raw_data[&spy_key]
+                .iter()
+                .enumerate()
+                .map(|(i, bar)| (bar.timestamp, i))
+                .collect()
+        } else {
+            HashMap::new()
+        };
+
+        // Main simulation loop (identical to run())
+        for (day_num, current_date) in trading_dates.iter().enumerate() {
+            self.current_bar = day_num;
+            self.new_positions_this_bar = 0;
+            self.prune_old_day_trades(current_date.date_naive());
+
+            // Get current prices and momentum for all symbols
+            let mut current_prices: HashMap<String, f64> = HashMap::new();
+            let mut momentum_scores: HashMap<String, f64> = HashMap::new();
+
+            for (symbol, rows) in &data {
+                if let Some(&idx) =
+                    symbol_date_index.get(symbol).and_then(|m| m.get(current_date))
+                {
+                    let row = &rows[idx];
+                    current_prices.insert(symbol.clone(), row.close);
+                    if !row.momentum.is_nan() {
+                        momentum_scores.insert(symbol.clone(), row.momentum);
+                    }
+                }
+            }
+
+            let portfolio_value = self.get_portfolio_value(&current_prices);
+
+            // SPY Market Regime Detection
+            let regime = if has_spy_data {
+                if let (Some(&spy_raw_idx), Some(spy_indicator_row)) = (
+                    spy_raw_date_index.get(current_date),
+                    data.get(&spy_key)
+                        .and_then(|rows| {
+                            symbol_date_index
+                                .get(&spy_key)
+                                .and_then(|m| m.get(current_date))
+                                .map(|&i| &rows[i])
+                        }),
+                ) {
+                    let ema50 = if spy_raw_idx < spy_ema50_series.len() {
+                        spy_ema50_series[spy_raw_idx]
+                    } else {
+                        f64::NAN
+                    };
+                    let ema200 = if spy_raw_idx < spy_ema200_series.len() {
+                        spy_ema200_series[spy_raw_idx]
+                    } else {
+                        f64::NAN
+                    };
+                    determine_market_regime(spy_indicator_row, ema50, ema200)
+                } else {
+                    MarketRegime::Caution
+                }
+            } else {
+                MarketRegime::Bull
+            };
+            self.current_regime = regime;
+
+            // Regime-based sizing factor
+            let regime_size_factor = match regime {
+                MarketRegime::Bull => 1.0,
+                MarketRegime::Caution => REGIME_CAUTION_SIZE_FACTOR,
+                MarketRegime::Bear => 0.0,
+            };
+
+            if day_num % 100 == 0 {
+                tracing::info!("  Market regime: {} (SPY)", regime.as_str());
+            }
+
+            // Update drawdown circuit breaker
+            self.update_drawdown_state(portfolio_value);
+
+            // Increment bars_held for all positions
+            for pos in self.positions.values_mut() {
+                pos.bars_held += 1;
+            }
+
+            // Momentum ranking
+            let mut ranked_symbols: Vec<String> = momentum_scores.keys().cloned().collect();
+            ranked_symbols.sort_by(|a, b| {
+                let ma = momentum_scores.get(a).unwrap_or(&0.0);
+                let mb = momentum_scores.get(b).unwrap_or(&0.0);
+                mb.partial_cmp(ma).unwrap_or(std::cmp::Ordering::Equal)
+            });
+            let top_momentum_symbols: HashSet<String> = ranked_symbols
+                .iter()
+                .take(TOP_MOMENTUM_COUNT)
+                .cloned()
+                .collect();
+
+            // Phase 1: Process sells
+            let position_symbols: Vec<String> = self.positions.keys().cloned().collect();
+            for symbol in position_symbols {
+                let rows = match data.get(&symbol) {
+                    Some(r) => r,
+                    None => continue,
+                };
+
+                let idx = match symbol_date_index
+                    .get(&symbol)
+                    .and_then(|m| m.get(current_date))
+                {
+                    Some(&i) => i,
+                    None => continue,
+                };
+
+                if idx < 1 {
+                    continue;
+                }
+
+                let current_row = &rows[idx];
+                let previous_row = &rows[idx - 1];
+
+                if current_row.rsi.is_nan() || current_row.macd.is_nan() {
+                    continue;
+                }
+
+                let mut signal = generate_signal(&symbol, current_row, previous_row);
+
+                // Check stop-loss/take-profit/trailing stop/time exit
+                if let Some(sl_tp) =
+                    self.check_stop_loss_take_profit(&symbol, signal.current_price)
+                {
+                    signal.signal = sl_tp;
+                }
+
+                let was_stop_loss = matches!(signal.signal, Signal::StrongSell);
+
+                if signal.signal.is_sell() {
+                    self.execute_sell(&symbol, signal.current_price, *current_date, was_stop_loss, portfolio_value);
+                }
+            }
+
+            // Phase 2: Process buys
+            if regime.allows_new_longs() {
+                let buy_threshold_bump = match regime {
+                    MarketRegime::Caution => REGIME_CAUTION_THRESHOLD_BUMP,
+                    _ => 0.0,
+                };
+
+                for symbol in &ranked_symbols {
+                    if symbol == REGIME_SPY_SYMBOL {
+                        continue;
+                    }
+
+                    let rows = match data.get(symbol) {
+                        Some(r) => r,
+                        None => continue,
+                    };
+
+                    if !top_momentum_symbols.contains(symbol) {
+                        continue;
+                    }
+
+                    let idx = match symbol_date_index
+                        .get(symbol)
+                        .and_then(|m| m.get(current_date))
+                    {
+                        Some(&i) => i,
+                        None => continue,
+                    };
+
+                    if idx < 1 {
+                        continue;
+                    }
+
+                    let current_row = &rows[idx];
+                    let previous_row = &rows[idx - 1];
+
+                    if current_row.rsi.is_nan() || current_row.macd.is_nan() {
+                        continue;
+                    }
+
+                    let signal = generate_signal(symbol, current_row, previous_row);
+
+                    let effective_buy = if buy_threshold_bump > 0.0 {
+                        let approx_score = signal.confidence * 10.0;
+                        approx_score >= (4.0 + buy_threshold_bump) && signal.signal.is_buy()
+                    } else {
+                        signal.signal.is_buy()
+                    };
+
+                    if effective_buy {
+                        self.execute_buy(
+                            symbol,
+                            signal.current_price,
+                            *current_date,
+                            portfolio_value,
+                            &signal,
+                            regime_size_factor,
+                        );
+                    }
+                }
+            }
+
+            // Record equity
+            self.equity_history.push(EquityPoint {
+                date: *current_date,
+                portfolio_value: self.get_portfolio_value(&current_prices),
+                cash: self.cash,
+                positions_count: self.positions.len(),
+            });
+
+            // Progress update
+            if (day_num + 1) % 100 == 0 {
+                tracing::info!(
+                    "  Processed {}/{} days... Portfolio: ${:.2} (positions: {})",
+                    day_num + 1,
+                    trading_dates.len(),
+                    self.equity_history
+                        .last()
+                        .map(|e| e.portfolio_value)
+                        .unwrap_or(0.0),
+                    self.positions.len()
+                );
+            }
+        }
+
+        // Close all remaining positions at final prices
+        let final_date = trading_dates.last().copied().unwrap_or_else(Utc::now);
+        let position_symbols: Vec<String> = self.positions.keys().cloned().collect();
+
+        for symbol in position_symbols {
+            if let Some(rows) = data.get(&symbol) {
+                if let Some(last_row) = rows.last() {
+                    self.execute_sell(&symbol, last_row.close, final_date, false, f64::MAX);
+                }
+            }
+        }
+
+        // Calculate results
+        let result = self.calculate_results(years)?;
+
+        // Print summary
+        self.print_summary(&result);
+
+        Ok(result)
+    }
+
     /// Calculate performance metrics from backtest.
     fn calculate_results(&self, years: f64) -> Result<BacktestResult> {
         if self.equity_history.is_empty() {

src/config.rs

@@ -73,11 +73,17 @@ pub const STOP_LOSS_PCT: f64 = 0.025;
 pub const MAX_LOSS_PCT: f64 = 0.08; // Gap protection only — ATR stop handles normal exits
 pub const TRAILING_STOP_ACTIVATION: f64 = 0.04; // Activate earlier to protect profits
 pub const TRAILING_STOP_DISTANCE: f64 = 0.05; // Wider trail to let winners run
-// ATR-based risk management
+// ATR-based risk management (DAILY timeframe - wider stops for longer-term holds)
 pub const RISK_PER_TRADE: f64 = 0.015; // 1.5% risk per trade (8 positions * 1.5% = 12% worst-case)
-pub const ATR_STOP_MULTIPLIER: f64 = 3.5; // Wide stops reduce false stop-outs (the #1 loss source)
+pub const ATR_STOP_MULTIPLIER: f64 = 3.5; // Wide stops reduce false stop-outs on daily
 pub const ATR_TRAIL_MULTIPLIER: f64 = 3.0; // Wide trail so winners run longer
 pub const ATR_TRAIL_ACTIVATION_MULTIPLIER: f64 = 2.0; // Don't activate trail too early
+
+// ATR-based risk management (HOURLY timeframe - much tighter to prevent 70-90% losses)
+// Hourly intraday noise requires stops 40-50% tighter than daily to avoid catastrophic drawdowns
+pub const HOURLY_ATR_STOP_MULTIPLIER: f64 = 1.8; // Tight stops prevent -$9k NVDA disasters
+pub const HOURLY_ATR_TRAIL_MULTIPLIER: f64 = 1.5; // Tight trail locks in hourly gains quickly
+pub const HOURLY_ATR_TRAIL_ACTIVATION_MULTIPLIER: f64 = 1.2; // Activate trail early on hourly
 // Portfolio-level controls
 pub const MAX_CONCURRENT_POSITIONS: usize = 8; // Fewer positions = higher conviction per trade
 pub const MAX_SECTOR_POSITIONS: usize = 2;

src/main.rs

@@ -50,7 +50,8 @@ use crate::config::{Timeframe, DEFAULT_INITIAL_CAPITAL};
                   Backtest 6 months:   invest-bot --backtest --months 6\n  \
                   Backtest 1y 6m:      invest-bot --backtest --years 1 --months 6\n  \
                   Custom capital:      invest-bot --backtest --years 5 --capital 50000\n  \
-                  Hourly backtest:     invest-bot --backtest --years 1 --timeframe hourly"
+                  Hourly backtest:     invest-bot --backtest --years 1 --timeframe hourly\n  \
+                  Custom date range:   invest-bot --backtest --start-date 2007-01-01 --end-date 2008-12-31"
 )]
 struct Args {
     /// Run in backtest mode instead of live trading
@@ -65,6 +66,14 @@ struct Args {
     #[arg(short, long, default_value_t = 0.0)]
     months: f64,
 
+    /// Start date for backtest (YYYY-MM-DD). Overrides --years/--months if provided.
+    #[arg(long, value_name = "YYYY-MM-DD")]
+    start_date: Option<String>,
+
+    /// End date for backtest (YYYY-MM-DD). Defaults to now if not provided.
+    #[arg(long, value_name = "YYYY-MM-DD")]
+    end_date: Option<String>,
+
     /// Initial capital for backtesting
     #[arg(short, long, default_value_t = DEFAULT_INITIAL_CAPITAL)]
     capital: f64,
@@ -171,14 +180,45 @@ async fn main() -> Result<()> {
 }
 
 async fn run_backtest(api_key: String, api_secret: String, args: Args) -> Result<()> {
-    // Combine years and months (default to 1 year if neither specified)
+    use chrono::NaiveDate;
-    let total_years = args.years + (args.months / 12.0);
-    let total_years = if total_years <= 0.0 { 1.0 } else { total_years };
 
     let client = AlpacaClient::new(api_key, api_secret)?;
     let mut backtester = Backtester::new(args.capital, args.timeframe);
 
-    let result = backtester.run(&client, total_years).await?;
+    let result = if args.start_date.is_some() || args.end_date.is_some() {
+        // Custom date range mode
+        let start_date = if let Some(ref s) = args.start_date {
+            NaiveDate::parse_from_str(s, "%Y-%m-%d")
+                .context("Invalid start date format. Use YYYY-MM-DD (e.g., 2007-01-01)")?
+        } else {
+            // If no start date provided, default to 1 year before end date
+            let end = if let Some(ref e) = args.end_date {
+                NaiveDate::parse_from_str(e, "%Y-%m-%d")?
+            } else {
+                chrono::Utc::now().date_naive()
+            };
+            end - chrono::Duration::days(365)
+        };
+
+        let end_date = if let Some(ref e) = args.end_date {
+            NaiveDate::parse_from_str(e, "%Y-%m-%d")
+                .context("Invalid end date format. Use YYYY-MM-DD (e.g., 2008-12-31)")?
+        } else {
+            chrono::Utc::now().date_naive()
+        };
+
+        // Validate date range
+        if start_date >= end_date {
+            anyhow::bail!("Start date must be before end date");
+        }
+
+        backtester.run_with_dates(&client, start_date, end_date).await?
+    } else {
+        // Years/months mode (existing behavior)
+        let total_years = args.years + (args.months / 12.0);
+        let total_years = if total_years <= 0.0 { 1.0 } else { total_years };
+        backtester.run(&client, total_years).await?
+    };
 
     // Save results to CSV
     save_backtest_results(&result)?;

src/strategy.rs

@@ -5,6 +5,7 @@ use crate::config::{
     ATR_TRAIL_ACTIVATION_MULTIPLIER, ATR_TRAIL_MULTIPLIER, MAX_LOSS_PCT, MAX_POSITION_SIZE,
     MIN_ATR_PCT, RISK_PER_TRADE, STOP_LOSS_PCT, TIME_EXIT_BARS,
     TRAILING_STOP_ACTIVATION, TRAILING_STOP_DISTANCE,
+    HOURLY_ATR_STOP_MULTIPLIER, HOURLY_ATR_TRAIL_MULTIPLIER, HOURLY_ATR_TRAIL_ACTIVATION_MULTIPLIER,
 };
 use crate::types::{Signal, TradeSignal};
 
@@ -14,6 +15,7 @@ pub struct Strategy {
     pub high_water_marks: HashMap<String, f64>,
     pub entry_atrs: HashMap<String, f64>,
     pub entry_prices: HashMap<String, f64>,
+    pub timeframe: Timeframe,
 }
 
 impl Strategy {
@@ -23,6 +25,7 @@ impl Strategy {
             high_water_marks: HashMap::new(),
             entry_atrs: HashMap::new(),
             entry_prices: HashMap::new(),
+            timeframe,
         }
     }
 
@@ -105,8 +108,14 @@ impl Strategy {
         }
 
         // 2. ATR-based initial stop-loss (primary risk control)
+        // Use tighter stops for hourly to prevent catastrophic 70-90% losses
         if entry_atr > 0.0 {
-            let atr_stop_price = entry_price - ATR_STOP_MULTIPLIER * entry_atr;
+            let stop_multiplier = if self.timeframe == Timeframe::Hourly {
+                HOURLY_ATR_STOP_MULTIPLIER
+            } else {
+                ATR_STOP_MULTIPLIER
+            };
+            let atr_stop_price = entry_price - stop_multiplier * entry_atr;
             if current_price <= atr_stop_price {
                 return Some(Signal::StrongSell);
             }
@@ -116,10 +125,15 @@ impl Strategy {
         }
 
         // 4. ATR-based trailing stop (profit protection)
-        // Activates earlier than before (1.5x ATR gain) so profits are locked in.
+        // Hourly uses much tighter trail to lock in gains quickly
-        // Distance is wider (2.5x ATR from HWM) so normal retracements don't trigger it.
+        let (trail_activation_mult, trail_mult) = if self.timeframe == Timeframe::Hourly {
+            (HOURLY_ATR_TRAIL_ACTIVATION_MULTIPLIER, HOURLY_ATR_TRAIL_MULTIPLIER)
+        } else {
+            (ATR_TRAIL_ACTIVATION_MULTIPLIER, ATR_TRAIL_MULTIPLIER)
+        };
+
         let activation_gain = if entry_atr > 0.0 {
-            (ATR_TRAIL_ACTIVATION_MULTIPLIER * entry_atr) / entry_price
+            (trail_activation_mult * entry_atr) / entry_price
         } else {
             TRAILING_STOP_ACTIVATION
         };
@@ -127,7 +141,7 @@ impl Strategy {
         if pnl_pct >= activation_gain {
             if let Some(&high_water) = self.high_water_marks.get(symbol) {
                 let trail_distance = if entry_atr > 0.0 {
-                    ATR_TRAIL_MULTIPLIER * entry_atr
+                    trail_mult * entry_atr
                 } else {
                     high_water * TRAILING_STOP_DISTANCE
                 };