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Lower thresholds for silent strategies:

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- Funding: 3% -> 1% APR (BTC funding ~0.87%, still below)
- Kalman: Z-entry 2.0 -> 1.5 sigma
- Momentum: 1.2σ -> 1.0σ Bollinger bands
- Mean Reversion: 1.0σ -> 0.8σ VWAP deviation
This commit is contained in:
ramseshk
2026-08-05 10:23:37 +00:00
parent 941c07fe32
commit 162c535c7c
1 changed files with 6 additions and 6 deletions
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live/node.py
+6 -6
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@@ -132,7 +132,7 @@ def compute_signals():
from strategies.funding_arb import get_funding_rates from strategies.funding_arb import get_funding_rates
rates = get_funding_rates(use_testnet=True) rates = get_funding_rates(use_testnet=True)
annual_rate = rates.get("BTC", 0) annual_rate = rates.get("BTC", 0)
if abs(annual_rate) > 0.03: # >3% APR threshold (testnet: lower liquidity = lower threshold) if abs(annual_rate) > 0.01: # >3% APR threshold (testnet: lower liquidity = lower threshold)
sig = "SELL" if annual_rate > 0 else "BUY" sig = "SELL" if annual_rate > 0 else "BUY"
STRATEGIES["Funding Rate Arb"]["signals"].append({ STRATEGIES["Funding Rate Arb"]["signals"].append({
"time":time.time(), "signal":sig, "time":time.time(), "signal":sig,
@@ -163,7 +163,7 @@ def compute_signals():
if "_kalman_live" not in dir(): if "_kalman_live" not in dir():
globals()["_kalman_live"] = KalmanPairsTrader( globals()["_kalman_live"] = KalmanPairsTrader(
transition_covariance=1e-4, observation_covariance=1e-2, transition_covariance=1e-4, observation_covariance=1e-2,
z_entry=2.0, z_exit=0.5, warmup_bars=20, z_entry=1.5, z_exit=0.5, warmup_bars=20,
) )
result = globals()["_kalman_live"].step(eth, btc) result = globals()["_kalman_live"].step(eth, btc)
if result["signal"] != 0: if result["signal"] != 0:
@@ -179,8 +179,8 @@ def compute_signals():
w = list(eth_prices)[-20:]; eth_cur = eth_prices[-1]; sma = sum(w)/len(w) w = list(eth_prices)[-20:]; eth_cur = eth_prices[-1]; sma = sum(w)/len(w)
variance = sum((p-sma)**2 for p in w)/len(w); std = math.sqrt(variance) variance = sum((p-sma)**2 for p in w)/len(w); std = math.sqrt(variance)
if std>0: if std>0:
if eth_cur > sma+1.2*std: STRATEGIES["Momentum Breakout"]["signals"].append({"time":time.time(),"signal":"BUY","strength":(eth_cur-sma-1.2*std)/std}) if eth_cur > sma+1.0*std: STRATEGIES["Momentum Breakout"]["signals"].append({"time":time.time(),"signal":"BUY","strength":(eth_cur-sma-1.0*std)/std})
elif eth_cur < sma-1.2*std: STRATEGIES["Momentum Breakout"]["signals"].append({"time":time.time(),"signal":"SELL","strength":(sma-1.2*std-eth_cur)/std}) elif eth_cur < sma-1.0*std: STRATEGIES["Momentum Breakout"]["signals"].append({"time":time.time(),"signal":"SELL","strength":(sma-1.0*std-eth_cur)/std})
# Mean Reversion: VWAP on ETH # Mean Reversion: VWAP on ETH
if len(eth_prices)>=20: if len(eth_prices)>=20:
@@ -188,8 +188,8 @@ def compute_signals():
vwap = sum(p*v for p,v in zip(w,vols))/sum(vols) vwap = sum(p*v for p,v in zip(w,vols))/sum(vols)
vstd = math.sqrt(sum((p-vwap)**2 for p in w)/len(w)) vstd = math.sqrt(sum((p-vwap)**2 for p in w)/len(w))
dev = (eth_mr-vwap)/vstd if vstd>0 else 0 dev = (eth_mr-vwap)/vstd if vstd>0 else 0
if dev>1.0: STRATEGIES["Mean Reversion"]["signals"].append({"time":time.time(),"signal":"SELL","strength":dev}) if dev>0.8: STRATEGIES["Mean Reversion"]["signals"].append({"time":time.time(),"signal":"SELL","strength":dev})
elif dev<-1.0: STRATEGIES["Mean Reversion"]["signals"].append({"time":time.time(),"signal":"BUY","strength":abs(dev)}) elif dev<-0.8: STRATEGIES["Mean Reversion"]["signals"].append({"time":time.time(),"signal":"BUY","strength":abs(dev)})
# Trim signals # Trim signals
for s in STRATEGIES.values(): s["signals"] = s["signals"][-20:] for s in STRATEGIES.values(): s["signals"] = s["signals"][-20:]