Jake’s Portfolio Daily Report - V3

Published

December 21, 2024

What to do Today

Today’s Buys

Token	Prediction Date	Decision	Model On
BNB	2024-12-21	buy	TRUE
NEAR	2024-12-21	buy	TRUE
TON	2024-12-21	buy	TRUE

A buy decision means the token price is predicted to rise by 10% or more in the next 30 days

Today’s Sells

Token	Prediction Date	Decision	Model On
ADA	2024-12-21	sell	TRUE
AVAX	2024-12-21	sell	TRUE
BCH	2024-12-21	sell	TRUE
BTC	2024-12-21	sell	TRUE
CRO	2024-12-21	sell	TRUE
DOGE	2024-12-21	sell	TRUE
DOT	2024-12-21	sell	TRUE
ETC	2024-12-21	sell	TRUE
ETH	2024-12-21	sell	TRUE
ICP	2024-12-21	sell	TRUE
LINK	2024-12-21	sell	TRUE
LTC	2024-12-21	sell	TRUE
PEPE	2024-12-21	sell	TRUE
SHIB	2024-12-21	sell	TRUE
SOL	2024-12-21	sell	TRUE
UNI	2024-12-21	sell	TRUE
XRP	2024-12-21	sell	TRUE

A sell decision means the token price is predicted to decline by 10% or more in the next 30 days

Today’s Holds

Token	Prediction Date	Decision	model_on_buy	model_on_sell

A hold decision means the token is predicted to stay within 10% of the current price in the next 30 days

Portfolio Performance

Current Portfolio Summary

invested	available	buys	sells
$1,719.12	$1,227.31	631	187

token	bought_amount	sold_amount	bought_dollars	reinv_dollars	sold_dollars	price	own_amount	dollars_diff	own_value	plus_minus	perc_diff
ADA	106.5200000	106.5200000	5.048662	32.451276	42.19044	0.9506151	0.0000000	37.1417796	0.0000000	37.1417796	7.3567570
AVAX	4.8901000	5.3229000	36.897880	75.569579	147.68944	39.7816680	-0.4328000	110.7915575	-17.2175059	93.5740516	2.5360279
BCH	0.5369127	0.2752108	88.271413	113.059447	95.37186	449.0209012	0.2617019	7.1004450	117.5096320	124.6100770	1.4116697
BNB	0.0671300	0.0669900	27.500000	10.000000	39.84000	677.8485768	0.0001400	12.3400001	0.0948988	12.4348989	0.4521781
BTC	0.0119764	0.0121402	197.409061	545.053616	775.84444	97691.4343169	-0.0001637	578.4353762	-15.9940416	562.4413346	2.8491161
CRO	1063.1000000	1063.1000000	89.475299	9.025132	90.04075	0.1634985	0.0000000	0.5654529	0.0000000	0.5654529	0.0063197
DOGE	719.2900000	732.9900000	61.844461	21.635342	89.27405	0.3174023	-13.7000000	27.4295894	-4.3484116	23.0811778	0.3732133
DOT	29.3700000	29.3700000	82.558994	49.941108	133.94995	7.2935419	0.0000000	51.3909567	0.0000000	51.3909567	0.6224756
ETC	8.3752910	8.3752910	30.003739	137.987977	155.44540	27.4076637	0.0000000	125.4416620	0.0000000	125.4416620	4.1808677
ETH	0.0079860	0.0079860	12.492566	7.499574	20.27182	3468.6589201	0.0000000	7.7792576	0.0000000	7.7792576	0.6227109
ICP	10.7470000	10.7470000	67.593360	19.906642	97.75999	10.5324181	0.0000000	30.1666276	0.0000000	30.1666276	0.4462957
LINK	12.1471000	12.1471000	135.978862	17.502696	151.32457	23.3428381	0.0000000	15.3457110	0.0000000	15.3457110	0.1128537
LTC	1.9636289	1.9636289	30.002412	101.545158	145.57053	101.3270791	0.0000000	115.5681219	0.0000000	115.5681219	3.8519611
NEAR	68.3000000	68.3000000	278.970031	38.120016	368.08993	5.3461098	0.0000000	89.1199027	0.0000000	89.1199027	0.3194605
PEPE	29366000.0000000	29366000.0000000	194.518462	73.945821	294.71571	0.0000177	0.0000000	100.1972482	0.0000000	100.1972482	0.5151041
SHIB	17756093.0000000	17972446.0000000	58.000103	279.384701	312.08620	0.0000225	-216353.0000000	254.0861003	-4.8712739	249.2148265	4.2967997
SOL	0.7738000	0.7738000	92.778506	15.076757	113.19272	194.1219845	0.0000000	20.4142135	-0.0000002	20.4142133	0.2200317
TON	102.1410000	16.6440000	139.910814	474.440058	98.25586	5.4440633	85.4970000	-41.6549577	465.4510802	423.7961225	3.0290448
UNI	14.0573000	14.0573000	57.498313	47.502013	91.84991	13.6944462	0.0000000	34.3516018	0.0000000	34.3516018	0.5974367
XRP	64.6800000	64.4800000	32.367103	5.132921	39.32829	2.2739969	0.2000000	6.9611837	0.4547994	7.4159831	0.2291210

plot_dat <- invested_dat %>% 
    right_join(tibble(date = seq(as_date(min(invested_dat$date)), 
                                 Sys.Date(), 
                                 by = "days")), 
               by = "date") %>% 
    group_by(date) %>% 
    summarise(invested = sum(ind_invest),
              invested = ifelse(is.na(invested), 0, invested),
              reinvested = sum(reinvested),
              reinvested = ifelse(is.na(reinvested), 0, reinvested),
              returned = sum(total_paid[action == "sell"], na.rm = TRUE),
              returned = ifelse(is.na(returned), 0, returned),
              cash_available = min(total_available, na.rm = TRUE)) %>% 
    mutate(cash_available = ifelse(is.infinite(cash_available), 
                                      NA, 
                                      cash_available)) %>% 
    fill(cash_available, .direction = "down") %>% 
    mutate(total_invested = cumsum(invested)) %>% 
    left_join(
        all_dat %>% 
            inner_join(
                invested_dat %>% 
                    group_by(date, token) %>% 
                    summarise(amount_owned = ifelse(action == "buy", amount, amount * -1)) %>% 
                    ungroup() %>% 
                    group_by(token) %>% 
                    arrange(date) %>% 
                    mutate(total_owned = cumsum(amount_owned)) %>% 
                    right_join(tibble(date = rep(seq(as_date(min(invested_dat$date)), 
                                                     Sys.Date(), 
                                                     by = "days"), 
                                                 length(unique(invested_dat$token))),
                                      token = rep(unique(invested_dat$token), 
                                                  each = length(seq(as_date(min(invested_dat$date)), 
                                                                    Sys.Date(), 
                                                                    by = "days")))), 
                       by = c("date", "token")),
                by = c("token", "date")) %>% 
            select(token, date, price, total_owned) %>% 
            mutate(total_owned = ifelse(total_owned < 0, 0, total_owned),
                   value = price * total_owned) %>% 
            arrange(token, date) %>% 
            group_by(token) %>% 
            fill(total_owned, .direction = "down") %>% 
            filter(!is.na(total_owned)) %>% 
            mutate(value = price * total_owned) %>% 
            ungroup() %>% 
            group_by(date) %>% 
            summarise(port_value = sum(value)),
        by = "date"
    ) %>% 
    mutate(cash_available = ifelse(is.infinite(cash_available), 0, cash_available)) %>% 
    fill(cash_available, .direction = "down") %>% 
    mutate(total_value = cash_available + port_value,
           up_down = (total_value - total_invested) / total_invested)

Warning: There were 100 warnings in `summarise()`.
The first warning was:
ℹ In argument: `cash_available = min(total_available, na.rm = TRUE)`.
ℹ In group 4: `date = 2024-06-03`.
Caused by warning in `min()`:
! no non-missing arguments to min; returning Inf
ℹ Run `dplyr::last_dplyr_warnings()` to see the 99 remaining warnings.

Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
dplyr 1.1.0.
ℹ Please use `reframe()` instead.
ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
  always returns an ungrouped data frame and adjust accordingly.

`summarise()` has grouped output by 'date', 'token'. You can override using the
`.groups` argument.

plot_dat %>% 
        left_join(btc %>% select(date, btc_up_down), by = "date") %>% 
        left_join(eth %>% select(date, eth_up_down), by = "date") %>% 
        # left_join(spy %>% select(Date, spy_up_down), by = c("date" = "Date")) %>% 
        # left_join(qqq %>% select(Date, qqq_up_down), by = c("date" = "Date")) %>% 
        # fill(spy_up_down, .direction = "down") %>% 
        # fill(qqq_up_down, .direction = "down") %>% 
        select(date, 
               up_down, 
               btc_up_down, 
               eth_up_down, 
               # spy_up_down, 
               # qqq_up_down
               )%>% 
                pivot_longer(!date, names_to = "names", values_to = "values") %>% 
                mutate(names = case_when(names == "up_down" ~ "RLE Analytics",
                                         names == "btc_up_down" ~ "BTC",
                                         names == "eth_up_down" ~ "ETH",
                                         names == "spy_up_down" ~ "SPY",
                                         names == "qqq_up_down" ~ "QQQ"),
                       names = factor(names, levels = c("RLE Analytics",
                                                        "BTC",
                                                        "ETH",
                                                        "SPY",
                                                        "QQQ"))) %>%
        ggplot(aes(x = date, y = values, group = names, color = names)) +
            geom_line() + 
            scale_y_continuous(labels = percent_format()) + 
            ggtitle("Portfolio Performance") + 
            geom_vline(aes(xintercept = min(tracker$date[tracker$algo_version == "v1"]))) + 
            geom_text(aes(x = min(tracker$date[tracker$algo_version == "v1"]) + days(2), 
                          y = 0, 
                          label = "v1")) + 
            geom_vline(aes(xintercept = min(tracker$date[tracker$algo_version == "v2"]))) + 
            geom_text(aes(x = min(tracker$date[tracker$algo_version == "v2"]) + days(2), 
                          y = 0, 
                          label = "v2")) + 
            geom_vline(aes(xintercept = min(tracker$date[tracker$algo_version == "v3"]))) + 
            geom_text(aes(x = min(tracker$date[tracker$algo_version == "v3"]) + days(2), 
                          y = 0, 
                          label = "v3")) + 
            geom_vline(aes(xintercept = min(tracker$date[tracker$algo_version == "v4"]))) + 
            geom_text(aes(x = min(tracker$date[tracker$algo_version == "v4"]) + days(2), 
                          y = 0, 
                          label = "v4")) + 
            theme(panel.background = element_blank(),
                  panel.grid.major = element_line(color = "light gray"),
                  axis.ticks = element_blank(), 
                  axis.title = element_blank(), 
                  plot.background = element_rect(fill = NA, color = "black"), 
                  legend.position = "top",
                  legend.key = element_blank(),
                  legend.title = element_blank())

plot_dat %>% 
    filter(date >= "2024-09-01")

# A tibble: 112 × 9
   date       invested reinvested returned cash_available total_invested
   <date>        <dbl>      <dbl>    <dbl>          <dbl>          <dbl>
 1 2024-09-01     26.8       3.16     1.52            0            1193.
 2 2024-09-02     18.5       1.52     7.59            0            1212.
 3 2024-09-03     12.4       7.59    26.7             0            1224.
 4 2024-09-04      0         9.85    28.2            16.8          1224.
 5 2024-09-05      0        10.0     26.6            35.0          1224.
 6 2024-09-06      0        15.0     24.1            46.6          1224.
 7 2024-09-07      0         0        0              46.6          1224.
 8 2024-09-08      0         0        0              46.6          1224.
 9 2024-09-09      0         5.00    25.7            65.6          1224.
10 2024-09-10      0         5.00    49.6            86.3          1224.
# ℹ 102 more rows
# ℹ 3 more variables: port_value <dbl>, total_value <dbl>, up_down <dbl>